Development and validation of machine learning models for predicting functional outcomes following total knee arthroplasty

Development and validation of machine learning models for predicting functional outcomes following total knee arthroplasty | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Development and validation of machine learning models for predicting functional outcomes following total knee arthroplasty Jin Taek Lee, Min Sun Kim, Young Mo Kim, Jun Hwan Choi, Bo Ryun Kim, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9408042/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 16 You are reading this latest preprint version Abstract Objective To develop and validate an explainable machine learning framework for predicting early functional recovery at 3 months following total knee arthroplasty (TKA) and to identify key patient-specific predictors using artificial intelligence (AI). Methods This retrospective cohort study included 313 patients who underwent primary TKA for end-stage knee osteoarthritis. Nine postoperative functional and patient-reported outcomes were assessed at baseline and at 3 months after surgery, including pain, mobility, quality of life, walking endurance, and knee muscle strength. Pre–post comparisons were performed to characterize overall recovery patterns. To identify appropriate prediction targets, exploratory screening was performed using seven machine learning algorithms with nested cross-validation. Canonical correlation analysis was applied to examine structural relationships among selected outcomes. Model diagnostics, feature importance analyses, and odds ratio comparisons were subsequently performed. Machine learning model performance was evaluated using mean absolute error, root mean squared error, and coefficient of determination (R²). Local Interpretable Model-agnostic Explanations (LIME) were applied to enhance model interpretability. Results The mean age of the 313 patients was 71.8 ± 5.9 years, and 49 patients (15.7%) were male. All nine outcomes demonstrated significant postoperative improvement (all p ≤ 0.001), indicating global early recovery. Exploratory modeling identified the 6-minute walk test (6MWT) and knee extension peak torque normalized to body weight (Knee Ex PT(BW)) as the only outcomes demonstrating stable predictive performance. Although both improved significantly postoperatively, prediction of Knee Ex PT(BW) showed greater explanatory power (best R² = 0.208) than the 6MWT (best R² = 0.141). Baseline muscle strength, stair-climbing performance, and skeletal muscle index were consistently identified as key predictors across models, whereas demographic and comorbidity variables showed minimal contribution. LIME-based explanations revealed patient-specific patterns concordant with established biomechanical and functional relationships. Conclusion Explainable machine learning models can identify meaningful patterns of early functional recovery after TKA. The 6MWT and Knee Ex PT(BW) were informative indicators of postoperative function and prognosis, with strength-based outcomes demonstrating particularly clear and interpretable predictive patterns. Explainable AI enabled transparent identification of patient-specific factors, providing a novel framework to support individualized rehabilitation planning. Arthroplasty Replacement Knee Recovery of Function Artificial Intelligence Models Statistical Muscle Strength Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Total knee arthroplasty (TKA) is among the most commonly performed surgical procedures for alleviating pain and restoring function in patients with end-stage knee osteoarthritis [ 1 – 3 ]. Despite standardized surgical techniques and postoperative care, substantial interindividual variability exists in recovery trajectories, with marked differences in functional improvement, walking ability, and muscle strength [ 4 , 5 ]. Such variability has significant implications for activities of daily living, quality of life, and healthcare utilization [ 6 ]. In clinical practice, a subset of patients experience delayed functional recovery, persistent pain, or prolonged rehabilitation, underscoring the need for early identification of individuals at risk for suboptimal outcomes [ 7 ]. Accurate preoperative prognostic assessment could facilitate personalized rehabilitation strategies and more efficient allocation of healthcare resources [ 8 ]. However, postoperative recovery following TKA is influenced by complex interactions among demographic characteristics, baseline physical function, pain severity, muscle strength, and psychosocial factors [ 9 , 10 ]. Conventional prognostic approaches primarily rely on isolated clinical indicators—such as pain scores, range of motion, or functional tests—which fail to capture the multidimensional and nonlinear nature of postoperative recovery [ 6 , 7 ]. Furthermore, traditional statistical models often demonstrate limited explanatory power when applied to heterogeneous clinical populations, restricting their utility for individualized decision-making [ 9 ]. Machine learning (ML) techniques offer a promising alternative by enabling the integration of high-dimensional clinical, functional, and patient-reported data while modeling complex nonlinear relationships [ 11 ]. Recent studies have demonstrated the potential of ML-based models to improve outcome prediction after orthopedic surgery [ 12 , 13 ]. However, the “black-box” nature of many ML algorithms poses a significant barrier to clinical adoption, as clinicians require transparent and interpretable decision-support tools [ 14 , 15 ]. Explainable artificial intelligence methods have been developed to address this limitation [ 16 , 17 ]. Among these, Local Interpretable Model-agnostic Explanations (LIME) provide case-level interpretability by identifying features that contribute most strongly to individual predictions [ 18 , 19 ]. LIME’s low computational burden and local explanatory framework make it particularly suitable for time-sensitive clinical settings, such as perioperative rehabilitation planning [ 18 , 20 , 21 ]. Accordingly, the primary aim of this study was to develop and validate an explainable ML-based framework that integrates preoperative clinical, functional, lifestyle, and psychological variables to predict functional recovery at 3 months following TKA. Particular emphasis was placed on objectively measurable functional domains, including walking endurance and knee extensor strength. A secondary aim was to apply LIME to visually and quantitatively identify key predictors influencing individual patient outcomes, thereby facilitating early risk stratification and personalized rehabilitation planning. Methods Study design and participants This retrospective cohort study included consecutive patients who underwent TKA for end-stage knee osteoarthritis at OO University Hospital between September 2013 and January 2022. The study protocol was approved by the Institutional Review Board of OO University OO Hospital (IRB No. 2022AN0110), and the requirement for informed consent was waived due to the retrospective nature of the study. Inclusion and exclusion criteria Patients were eligible if they met the following criteria: (1) Diagnosis of end-stage knee osteoarthritis (Kellgren–Lawrence grade 3 or 4); (2) Completion of standardized preoperative baseline assessments; (3) Availability of postoperative functional evaluations at 3 months postoperatively; and (4) Ability to ambulate independently, with or without assistive devices. Exclusion criteria were as follows: (1) History of neurological or orthopedic disorders affecting gait or lower-limb function; (2) Missing essential preoperative predictor variables; or (3) Loss to follow-up before the 3-month postoperative assessment. A total of 313 patients met the eligibility criteria and were included in the final analysis. A flow diagram summarizing the patient selection process is provided in Figure S1 . Baseline multidimensional assessments Patient-reported assessments The Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) The WOMAC questionnaire is a disease-specific, self-administered instrument designed to assess knee and hip osteoarthritis. WOMAC evaluates pain, stiffness, and physical function [ 22 – 27 ] using 24 items (5 pain, 2 stiffness, and 17 function items), each rated on a 5-point Likert scale (0 = none to 4 = extreme). Higher scores indicate more severe symptoms and functional limitations. This tool captures the multidimensional impact of osteoarthritis on daily activities. Visual analog scale (VAS) Knee pain intensity was measured using a 10-cm horizontal VAS, anchored by “no pain” (0) and “worst possible pain” (10) [ 28 ]. Patients were instructed to indicate their resting pain level. The VAS provides a simple and sensitive measure of perceived pain severity that is widely used in clinical and research settings [ 29 ]. EuroQol-5 Dimension Questionnaire (EQ-5D) The EQ-5D is a standardized measure of general health-related quality of life [ 30 – 33 ], assessing five domains: mobility, self-care, usual activities, pain/discomfort, and anxiety/depression. Each domain is rated at three levels (no problems, some problems, or extreme problems). Responses are converted into a single index score that reflects overall health utility. Performance-based functional assessments Timed Up and Go (TUG) test The TUG test measures the time (seconds) required for a patient to rise from a chair, walk 3 m, turn, return, and sit down again [ 34 , 35 ]. Longer times reflect poorer balance and mobility. TUG provides a quick, reliable indicator of overall functional mobility and fall risk in older adults [ 36 , 37 ]. Each patient sat with their back against a standardized chair (seat height, 44 cm; depth, 45 cm; width, 49 cm; armrest height, 64 cm) positioned at the end of a 3-m walkway. On the command “go,” patients stood, walked at a comfortable pace to the 3-m mark, turned, walked back, and sat down again without physical assistance while being timed. Six-minute walk test (6MWT) The 6MWT measures the distance (m) a patient can walk in 6 minutes along a marked hallway. It evaluates aerobic capacity, endurance, and walking function by integrating cardiopulmonary fitness and lower-limb performance [ 38 – 40 ]. A greater distance indicates better overall locomotor ability. Patients were instructed to walk as far as possible for 6 minutes along a 50-m hallway marked with lines. Stair Climbing Test (SCT) The SCT evaluates functional mobility and lower-limb power by measuring the time required to ascend and descend a 12-step staircase (17-cm step height, 25-cm width). Shorter completion times reflect greater concentric and eccentric muscle performance [ 41 – 43 ]. This test reflects the capacity to perform daily mobility tasks that require high muscle power, such as stair negotiation. Isometric strength of the knee extensors and flexors (Knee Ex PT(BW) and Knee Fl PT(BW)) Maximal isometric torque of the bilateral knee extensors and flexors was measured using an isokinetic dynamometer (Computer Sports Medicine Inc., Stoughton, MA, USA). Before the test, patients performed light stretching to relax the muscles. During the procedure, patients were instructed to grasp the sidebars of the apparatus. Following a standardized warm-up, the knee joint was fixed at 60° of flexion (to generate maximal isometric force), and patients performed maximal voluntary contractions until the torque failed to increase by > 5% across three successive attempts. These measures provide an objective index of quadriceps and hamstring capacity, which are critical determinants of postoperative recovery [ 44 , 45 ]. Nine postoperative functional and patient-reported outcomes were assessed at baseline and at 3 months after surgery, including pain, mobility, quality of life, walking endurance, and knee extensor strength. Data preprocessing All variables were examined for completeness and consistency. Categorical variables were numerically encoded. Missing values were imputed using clinically informed encoding strategies (e.g., absence of documented comorbidities), while continuous variables were imputed using simple imputation methods prior to standardization. Continuous variables were then standardized to ensure compatibility with ML algorithms. The dataset comprised 313 samples and was randomly partitioned into a training set (n = 188), an internal validation set (n = 63), and an external validation test set (n = 63). The external test set was randomly held out from the same cohort. ML modeling and evaluation ML–based regression models were developed to predict functional recovery at 3 months following TKA. All outcomes were initially considered as potential prediction targets. An exploratory screening process identified outcomes demonstrating stable predictive performance. Outcomes demonstrating reproducible explanatory power and consistent performance across folds were retained for subsequent structural and interpretability analyses. Seven ML regressors were used: AdaBoost Regressor, Extra Trees Regressor, Gradient Boosting Regressor, Random Forest Regressor, Histogram-based Gradient Boosting Regressor, Support Vector Machine Regressor, and Tabular Prior-data Fitted Network (TabPFN) Regressor. Model training and hyperparameter optimization were performed using nested cross-validation, with five outer folds for performance evaluation and five inner folds for grid-search–based hyperparameter tuning (Supplementary Table 1). Model performance was assessed using mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), and the coefficient of determination (R²). For each outcome, the model achieving the highest mean R² across the outer validation folds was selected as the final predictive model. To enhance clinical interpretability, LIME was applied to the selected models [ 18 ]. LIME was used to identify key features contributing to individual predictions. Based on LIME rankings and clinical relevance, clinicians selected 4–6 representative features for interpretive analysis, integrating highly ranked model-derived features with additional clinically important variables. Statistical analysis Descriptive statistics were used to summarize baseline characteristics. Independent sample t -tests, chi-square tests, and canonical correlation analyses (CCA) were used to evaluate relationships between individual features and clinical outcomes. Odds ratios (ORs) were calculated to quantify the intensity of associations between categorical variables and clinical outcomes. Univariate associations were evaluated using Pearson’s correlation coefficients for continuous variables, and t -tests or chi-square tests for group comparisons, as appropriate. Variables with relevant univariable associations were included in the multivariable linear regression models. Statistical significance was defined as p < 0.05. Where external validation was available, both internally cross-validated performance and external validation results were reported. Results Cohort characteristics The final analytic cohort comprised 313 patients (mean age, 71.8 ± 5.9 years), including 49 males (15.7%) and 264 females (84.3%). Baseline demographic characteristics, comorbidities, and preoperative functional and patient-reported assessments are summarized in Tables 1 and 2 . The prevalence of major comorbidities included hypertension (65.5%), diabetes mellitus (17.6%), hyperlipidemia (25.6%), cardiovascular disease (5.4%), and prior stroke (1.6%). Table 1 Demographic and clinical characteristics of the patients (N = 313) Variables Values Age (years) 71.81 ± 5.91 Sex, male/female (number) 49 (15.7%) / 264 (84.3%) Height (cm) 153 ± 7.13 Weight (kg) 62.73 ± 9.62 Body mass index (kg/m 2 ) 26.53 ± 3.43 Skeletal muscle index (kg/m 2 ) 6.57 ± 0.78 Clinical characteristics (number) K–L grade 2 / 3 / 4 1 (0.3%) / 51 (16.3%) / 261 (83.4%) TKA surgery history 168 (53.7%) Stroke 5 (1.6%) Cardiovascular disease 17 (5.4%) Hypertension 205 (65.5%) Diabetes mellitus 55 (17.6%) Hyperlipidemia 80 (25.6%) K–L, Kellgren–Lawrence; TKA, total knee arthroplasty Table 2 Functional and patient-reported assessments of patients: baseline and 3 months after TKA (N = 313) Variables Baseline 3-month follow-up Mean difference (pre-post) t-value p -value WOMAC total 42.88 ± 10.24 26.97 ± 9.49 15.9 25.71 < 0.001 VAS score 6.82 ± 1.65 2.40 ± 1.17 4.42 39.65 < 0.001 EQ-5D 0.59 ± 0.14 0.80 ± 0.08 –0.22 –26.20 < 0.001 TUG 11.88 ± 3.38 8.98 ± 1.83 2.9 16.42 < 0.001 6MWT 309.77 ± 158.65 458.61 ± 94.98 –148.85 –15.18 < 0.001 SCT ascent 14.73 ± 6.59 10.48 ± 3.84 4.26 13.16 < 0.001 SCT descent 17.36 ± 6.71 12.19 ± 4.45 5.17 15.99 < 0.001 Knee Ex PT(BW) 79.25 ± 27.73 84.67 ± 23.73 –5.42 –3.70 < 0.001 Knee Fl PT(BW) 47.89 ± 15.68 52.40 ± 13.82 –4.52 –5.74 < 0.001 WOMAC, The Western Ontario and McMaster Universities Osteoarthritis Index; VAS, visual analog scale; EQ-5D, EuroQol-5 dimension Questionnaires; TUG, timed up and go test; 6MWT, 6-minute walk test; SCT, stair climbing test; Knee Ex PT(BW), knee extension peak torque normalized to body weight; Knee Fl PT(BW), knee flexor peak torque normalized to body weight; TKA, total knee arthroplasty. Baseline-to-3-month changes in functional outcomes: univariate analysis prior to ML modeling Table 2 presents univariable analyses of associations between preoperative and 3-month postoperative functional outcomes following TKA. All functional and patient-reported outcome measures demonstrated statistically significant improvements at 3 months (all p ≤ 0.001), indicating global recovery in pain, mobility, walking endurance, stair performance, and muscle strength. Patient-reported outcomes, including the WOMAC total score, VAS pain score, and EQ-5D index, showed marked postoperative improvement, reflecting substantial reductions in pain and perceived functional limitations. Performance-based measures, such as the TUG test, SCT (ascent and descent), and 6MWT, also demonstrated significant gains, indicating enhanced mobility and functional capacity during the early postoperative period. Additionally, both knee extensor peak torque normalized to body weight (Knee Ex PT(BW)) and flexor peak torque normalized to body weight (Knee Fl PT(BW)) significantly increased at 3 months, reflecting recovery of lower-limb muscle strength following surgery. These univariate pre–post comparisons were performed to characterize overall patterns of early postoperative recovery rather than to identify predictive targets. Although all assessed variables demonstrated statistically significant improvement, statistical significance alone does not imply suitability for outcome prediction. Therefore, subsequent analyses focused on identifying functional outcomes with meaningful predictive potential when multiple preoperative factors were considered simultaneously using ML approaches. Model performance and stability–guided outcome selection All nine postoperative functional and patient-reported assessments were initially evaluated as potential prediction targets using ML regression models. Although several outcomes demonstrated statistically significant model correlations, exploratory analyses revealed that most patient-reported and mobility-based outcomes showed limited predictive performance. Specifically, they exhibited low R² values, unstable validation results, and heterogeneous residual distributions across models, limiting their suitability as reliable prediction targets. In contrast, the 6MWT and Knee Ex PT(BW) demonstrated comparatively greater explanatory power, more reproducible performance across ML algorithms, and greater residual stability. For the 6MWT, the support vector machine regressor achieved the highest explanatory power (R² = 0.141). Knee Ex PT(BW) demonstrated greater maximal predictability with the TabPFN regressor (R² = 0.208). Importantly, feature-importance patterns for both outcomes were directionally consistent with established biomechanical and rehabilitation principles. These two outcomes also represent clinically meaningful domains of early postoperative recovery, reflecting integrated functional mobility and objective neuromuscular capacity, respectively. Accordingly, subsequent multivariate structural analyses (CCA), model diagnostics, feature-importance evaluation, and OR comparisons focused on these two clinically and statistically stable prediction targets. Structural interdependence of functional recovery domains following TKA CCA was performed to examine relationships among key functional variables measured preoperatively and at 3 months postoperatively. In all panels, the horizontal axis represents preoperative values and the vertical axis represents the corresponding 3-month postoperative values, enabling visualization of associations between baseline status and postoperative recovery. This analysis aimed to identify patterns of interrelationships among major functional indicators during early recovery following TKA. First, a statistically significant positive correlation was observed between preoperative SCT ascent performance and postoperative 6MWT performance at 3 months (Fig. 1 a). This finding indicates that better baseline stair-ascent ability is associated with superior postoperative walking endurance, suggesting that shared physical components, such as lower limb strength, balance, and mobility, contribute to functional recovery. Second, a significant positive association was also identified between preoperative SCT descent performance and postoperative 6MWT performance at 3 months (Fig. 1 b). Because stair descent requires coordinated control of knee extension and flexion, proprioceptive input, and balance, this correlation suggests that patients with better preoperative functional control tend to achieve more favorable postoperative endurance outcomes. Third, preoperative Knee Ex PT(BW) showed a moderate positive correlation with postoperative Knee Ex PT(BW) at 3 months (Fig. 1 c). This relationship suggests that baseline knee extensor muscle strength is closely linked to postoperative recovery of knee extensor strength, reflecting a coordinated and interdependent recovery pattern within the lower-limb musculature following TKA. Finally, a statistically significant association was observed between sex and postoperative Knee Ex PT(BW) at 3 months (Fig. 1 d), with higher extension strength values generally observed in male patients. This finding indicates that strength-related functional measures may vary by sex and should be interpreted accordingly. Taken together, these CCA-based analyses demonstrate that baseline functional status and patient characteristics are strongly associated with postoperative walking ability, stair performance, and lower-limb muscle strength. These results highlight that functional recovery domains following TKA are interrelated rather than independent, particularly during the early postoperative period. Domain-dependent predictive performance of ML models in early postoperative recovery Seven ML regression models (Ada Boost Regressor, Extra Trees Regressor, Gradient Boosting Regressor, Random Forest Regressor, Historically Based Gradient Boosting Regressor, Support Vector Machine Regressor, and TabPFN Regressor) were trained to predict functional outcomes at 3 months postoperatively. Performance metrics, including MAE, MSE, RMSE, and R², are summarized in Supplementary Table 2. Prediction of the 6MWT showed modest overall model performance, with R² values remaining below 0.15 across all models. The Support Vector Machine Regressor demonstrated the highest explanatory power (R² = 0.141) and the lowest RMSE (0.129), followed by the Random Forest and Extra Trees regressors, which showed comparable error magnitudes but lower R² values. Overall, the 6MWT was more difficult to predict than other functional indicators, with R² values falling below 0.10 in several models, indicating limited predictive capacity. These findings suggest that walking endurance is a complex outcome influenced by multiple physiological and behavioral factors, which constrain predictive accuracy. In contrast, the prediction of Knee Ex PT(BW) demonstrated comparatively greater explanatory power. The TabPFN Regressor achieved the best performance (R² = 0.208, RMSE = 0.123), followed closely by the Support Vector Machine Regressor (R² = 0.196). Tree-based ensemble models exhibited variable performance, with some showing limited or negative R² values. Overall, strength-based outcomes demonstrated greater predictability than walking-based outcomes. These findings indicate that optimal model performance depends on the prediction target and that complex functional measures, such as 6MWT, have relatively greater predictive difficulty due to the influence of multiple biological and environmental factors. Residual analysis and model stability assessment for postoperative outcome prediction Residual distribution analysis is shown in Fig. 2 . Residuals for the 6MWT were right-skewed and with several extreme values, suggesting heterogeneous individual walking recovery. In contrast, residuals for Knee Ex PT(BW) were more symmetrically distributed around zero, suggesting greater model stability for strength prediction. Figure 3 presents Q–Q plots assessing residual normality for both outcomes. Figure 3 a (6MWT), shows limited residual normality, with segments deviating from the theoretical quantile line, a pattern commonly observed when predicting walking ability from complex biomechanical factors. In contrast, Fig. 3 b (Knee Ex PT(BW)) indicates residuals that closely follow the reference line, indicating that strength-based indicators exhibit a more stable and consistent structure, and are therefore easier to model. Figure 4 illustrates prediction accuracy by directly comparing the actual value with the model-predicted value. In Fig. 4 a, discrepancies appear between the measured and predicted values in some instances; however, both series follow a similar overall trend. This finding indicates that the model captures the general pattern of walking ability, although prediction errors may arise for cases with large individual variability. In Fig. 4 b, the observed and predicted curves remain closely aligned in most instances, indicating greater prediction stability. Notably, the location and direction of peak changes show strong agreement, suggesting higher reliability of the model in predicting the strength index. Overall, these results indicate that the muscle strength index (Knee EX PT(BW)) demonstrates better model fit and greater predictive stability than the walking index (6MWT). The 6MWT is more difficult to predict because it reflects its multiple interacting factors, including physiological status, cardiopulmonary endurance, balance, and pain sensitivity, whereas Knee Ex PT(BW) is a more localized, structural indicator that allows for more precise model-based prediction. Explainable ML identifies structural determinants of strength and walking recovery Figure 5 illustrates the importance of permutation-based features in models predicting functional outcomes (6MWT, Knee Ex PT(BW)) at 3 months post-surgery. Each subpanel quantifies the contribution of input variables to the predictive performance of the corresponding clinical indicators. In the 6MWT prediction model (Fig. 5 a), preoperative muscle strength-related variables—specifically Knee Fl PT(BW) and Knee Ex PT(BW)—had the highest importance scores (0.8682 and 0.2117, respectively). This finding is consistent with prior evidence indicating that gait endurance recovery is intrinsically dependent on lower-limb muscle strength. Additionally, preoperative SCT descent (0.1207) and SCT ascent (0.0795) demonstrated significant relative importance, reflecting shared biomechanical demands (balance, strength, and proprioception) between stair negotiation and walking ability. Other variables, including preoperative TUG, 6MWT, EQ-5D, and VAS, contributed minimally, whereas demographic and medical factors (age, stroke, hypertension, etc.) had negligible impact on the prediction. Similarly, in the Knee Ex PT(BW) model (Fig. 5 b), preoperative knee muscle strength indicators were the dominant predictors. Knee Fl PT(BW) (1.0767) and Knee Ex PT(BW) (0.9804) ranked highest, demonstrating that postoperative knee extension strength recovery is closely linked to the preoperative status of the entire knee muscle group (agonist and antagonist). The high importance of knee flexor strength highlights the integrated biomechanical relationship required for functional movement. Preoperative SCT descent and ascent also emerged as important predictors, supporting the role of stair performance as a strong indicator of muscle function. In contrast, preoperative 6MWT, TUG, and EQ-5D showed only indirect effects, suggesting that local muscle strength recovery is driven more by strength-specific metrics than by general mobility measures. Overall, the feature importance analysis reveals a consistent pattern across both functional outcomes: preoperative knee muscle strength and stair performance are the primary determinants of functional recovery at 3 months, whereas demographic and medical history variables contribute little predictive value. These findings suggest that ML models for TKA recovery should prioritize functional and muscle-specific evaluations over static risk factors to significantly improve prediction accuracy. WOMAC, The Western Ontario and McMaster Universities Osteoarthritis Index; VAS, visual analog scale; EQ-5D, EuroQol-5 dimension Questionnaires; TUG, timed up and go test; 6MWT, 6-minute walk test; SCT, stair climbing test; Knee Ex PT(BW), knee extension peak torque normalized to body weight; Knee Fl PT(BW), knee flexor peak torque normalized to body weight; TKA, total knee arthroplasty; BMI, body mass index; SMI, skeletal muscle index; CI, confidence interval. Figure 6a–b presents the LIME analysis results for the best-performing models using external validation data. The length of each bar represents the magnitude of a feature's contribution to the prediction of each functional outcome (6MWT, Knee Ex PT(BW)). In Fig. 6a (6MWT), key predictors included preoperative 6MWT, SCT (ascent/descent), and skeletal muscle index (SMI), indicating that gait endurance recovery is intrinsically linked to systemic muscle mass and stair negotiation ability—a functional task requiring lower-limb strength, balance, and coordination. In Fig. 6b (Knee Ex PT(BW)), the most influential variables included preoperative Knee Ex PT(BW), SMI, Knee Fl PT(BW), and SCT (ascent/descent). This highlights that knee extensor strength recovery follows an integrated pattern structurally associated with knee flexor strength, muscle mass, and functional stair performance. Notably, SCT ascent/descent demonstrated strong explanatory power across both models, serving as a dual indicator of knee muscle strength and actual functional performance. Collectively, the LIME analysis results demonstrate that while walking ability and knee muscle strength share common functional predictors (e.g., SMI and SCT), they also retain distinct domain-specific determinants. The model's predictive logic largely aligns with established biomechanical relationships observed in clinical practice. These findings confirm that LIME-based interpretations provide valuable insights for identifying individual recovery patterns and establishing personalized rehabilitation strategies. Comparative evaluation of traditional univariable risk estimation and ML prediction Figure 6 Model performance for predicting functional outcomes on external data assessed using LIME analysis. Each panel corresponds to a specific outcome: (a) 6MWT (3M), (b) Knee Ex PT (BW) (3M). WOMAC, The Western Ontario and McMaster Universities Osteoarthritis Index; VAS, visual analog scale; EQ-5D, EuroQol-5 dimension Questionnaires; TUG, timed up and go test; 6MWT, 6-minute walk test; SCT, stair climbing test; Knee Ex PT(BW), knee extension peak torque normalized to body weight; Knee Fl PT(BW), knee flexor peak torque normalized to body weight; TKA, total knee arthroplasty; BMI, body mass index; SMI, skeletal muscle index; CI, confidence interval; LIME, Local Interpretable Model-agnostic Explanations. Supplementary Table 3 summarizes the ORs and 95% confidence intervals (CI) for variables associated with functional recovery (6MWT, Knee Ex PT(BW)) at 3 months post-surgery. No single variable reached the significance threshold ( p ≤ 0.001), indicating that individual clinical and functional factors alone are insufficient to predict functional recovery. Nevertheless, the direction and magnitude of the ORs provide insights into potential trends underlying recovery. For 6MWT recovery, most variables showed ORs ranging from 0.5 to 1.5, indicating that no factor emerged as a dominant independent predictor. Advanced age (OR, 0.650) showed a tendency toward lower recovery potential, though this association was not statistically significant. Major clinical variables (surgical history and comorbidities) and functional indicators (TUG, 6MWT, SCT, and knee muscle strength) also lacked significant predictive power (OR, 0.394–1.330). These findings underscore that walking endurance is a multifactorial outcome influenced by complex biomechanical and psychological interactions. Similarly, for Knee Ex PT(BW), most variables presented ORs between 0.7 and 1.5, reinforcing the difficulty of univariate prediction. Female sex (OR, 0.695) suggested a potential disadvantage in knee muscle strength restoration, while the SMI showed a relatively high association (OR, 2.129), reflecting a physiological link, although neither association reached statistical significance. This suggests that knee muscle strength recovery relies on a combination of factors—such as neuromuscular activity, pain control, and rehabilitation adherence—rather than simple baseline characteristics. In conclusion, these results demonstrate that functional recovery following TKA is a multidimensional process that cannot be adequately explained by single clinical variables. The limited explanatory power of univariate models highlights the complexity of the rehabilitation process. Therefore, these findings strongly support the necessity of adopting ML models capable of capturing high-dimensional interactions to improve prediction accuracy. Discussion This study developed and validated an explainable ML framework to predict early functional recovery following TKA using multidimensional preoperative data. At 3 months postoperatively, patients demonstrated significant improvements in pain, self-reported function, mobility, walking endurance, and knee muscle strength. Notably, the substantial gains in the 6MWT, along with improvements in stair performance and TUG, indicate meaningful restoration of functional mobility during the early postoperative period. Knee extensor and flexor strength also improved significantly, reflecting recovery of lower-limb muscle performance after surgery. Collectively, these findings confirm that multiple functional domains demonstrate measurable improvement within the first 3 months after TKA[ 4 , 46 ]. However, improvement in individual outcomes does not necessarily imply that these domains recover independently. Given the shared neuromuscular and biomechanical factors underlying postoperative function, muscle strength, mobility, and walking endurance may be structurally interrelated rather than isolated constructs [ 47 – 49 ]. To further examine the structural relationships among these outcomes, CCA was performed. Postoperative functional outcomes did not behave independently, but rather clustered into interconnected recovery patterns. Walking endurance was closely associated with stair ascent and descent performance, while knee extensor strength demonstrated coordinated recovery [ 48 , 49 ]. These findings indicate that early postoperative function after TKA is best understood as an integrated system rather than a collection of isolated measures. Consequently, impairments in a single domain, such as stair negotiation, may reflect broader limitations in overall mobility and lower-limb muscle performance. Building on this integrated recovery pattern, we examined whether different functional domains exhibited comparable levels of predictive accuracy when modeled using multidimensional preoperative data. ML analyses revealed that predictive performance differed across outcomes. Among the evaluated measures, knee extensor strength demonstrated greater predictability than the 6MWT. This finding is consistent with the physiological characteristics of the two outcomes. Knee extensor strength reflects a localized, structurally constrained function, largely determined by neuromuscular capacity and muscle mass, whereas walking endurance is a composite ability influenced by cardiopulmonary fitness, balance, pain perception, motivation, and environmental factors. Therefore, the lower predictive performance observed for the 6MWT likely reflects the inherent complexity of gait-related recovery rather than limitations of the modeling approach itself. The modest R 2 values observed in this study are comparable to those reported in previous prognostic studies of postoperative function and underscore the challenges of predicting complex functional behaviors in heterogeneous older populations. Differences in predictive performance were further supported by residual and model-fit analyses. Predictions of knee extensor strength exhibited relatively symmetric residual distributions and strong agreement between predicted and observed values, indicating stable and consistent model performance. In contrast, residuals for 6MWT were more heterogeneous, with deviations from normality and occasional extreme values. Clinically, this variability likely reflects individual differences in pain tolerance, balance confidence, cardiopulmonary reserve, and activity engagement during early recovery, underscoring the multifactorial determinants of walking endurance. Beyond overall predictive performance, examining influential predictors provides additional insight into the determinants of postoperative recovery. Feature importance analyses consistently identified preoperative knee muscle strength and stair-climbing performance as the most influential predictors of both walking endurance and knee extensor strength. Stair-climbing tasks integrate concentric and eccentric muscle control, balance, and coordination, making them sensitive indicators of lower-limb functional reserve. The repeated importance of SMI further emphasizes the role of systemic muscle mass in early postoperative recovery, particularly among older adults at risk of sarcopenia. Notably, knee flexor strength contributed substantially to the prediction of knee extensor strength recovery, highlighting the coordinated agonist-antagonist relationship required for stable knee function. This finding suggests that comprehensive knee muscle conditioning, rather than isolated quadriceps strengthening alone, may be critical for optimal recovery. To enhance individual-level interpretability, LIME was applied to generate patient-specific explanations of model predictions. The resulting explanations demonstrated clinically plausible patterns, illustrating how functional and strength-related factors influenced predicted outcomes for individual patients. The concordance between model-derived explanations and established biomechanical principles strengthens the clinical credibility of the proposed framework and may facilitate its integration into rehabilitation decision-making. However, not all postoperative outcomes demonstrated sufficient stability or explainability to support in-depth predictive modeling. Careful selection of prediction targets was also essential to ensure model stability and explainability. Although nine postoperative functional outcomes showed statistically significant improvement, only 6MWT and knee extensor strength demonstrated reproducible predictive performance and consistent validation behavior across algorithms. Most patient-reported and general mobility measures exhibited limited explanatory power and unstable validation results, suggesting that these outcomes may be influenced by contextual and psychosocial factors not fully captured by structured preoperative variables. Focusing on walking endurance and knee extensor strength enabled more robust multivariable modeling and clearer evaluation of the incremental value of ML beyond traditional analytical approaches. In contrast to multivariable ML models, univariate OR analyses failed to identify statistically significant predictors of postoperative recovery. This finding highlights the limitations of isolated demographic or clinical variables in explaining functional variability after TKA and further supports the multidimensional nature of rehabilitation. Rather than representing a negative finding, the absence of strong univariate associations underscores the need for analytical approaches capable of modeling nonlinear interactions among multiple contributing factors. Clinically, these findings suggest that modifiable functional characteristics—particularly knee muscle strength, stair negotiation ability, and muscle mass—play a more central role in early postoperative recovery than static demographic characteristics or medical comorbidities. This supports the implementation of targeted prehabilitation and early postoperative rehabilitation strategies emphasizing strength enhancement and task-specific functional training. Therefore, explainable ML models may serve as complementary decision-support tools to identify patients who would benefit most from individualized or intensified rehabilitation programs. Several limitations should be acknowledged. First, the study population included a higher proportion of female patients, consistent with the epidemiology of knee osteoarthritis and TKA in older populations. Although sex was identified as a contributing factor in some analyses, its overall predictive influence was modest, and the primary findings regarding functional recovery patterns remained robust. Second, this was a single-center retrospective study, which may limit generalizability. Third, predictive performance for walking endurance remained modest despite rigorous validation, reflecting the complexity of gait-related outcomes. Fourth, although nine postoperative outcomes were initially explored, only two demonstrated sufficient predictive stability for in-depth modeling, which may limit generalizability to other functional domains. Finally, only structured clinical data were analyzed. Future studies should incorporate longitudinal assessments, unstructured clinical data, and multicenter cohorts to further refine predictive accuracy and enhance clinical applicability. Conclusion This study demonstrates that an explainable ML framework can predict early functional recovery following TKA using multidimensional preoperative data. Knee extensor strength showed greater predictability than walking endurance, reflecting the more localized and structurally constrained nature of strength recovery. In addition, preoperative knee muscle strength, stair-climbing ability, and skeletal muscle mass emerged as key determinants of early postoperative function, whereas demographic characteristics and medical comorbidities contributed minimally. Explainable artificial intelligence enabled transparent, patient-specific explanations of predictions, supporting the potential use of these models as adjunctive tools for personalized rehabilitation planning after TKA. Abbreviations TKA total knee arthroplasty Declarations Acknowledgements: None Author contributions: B.R.K. conceptualized and designed the study. B.R.K., J.H.C. collected and curated the clinical data. S.H.C. developed the machine learning models and performed the statistical analysis. B.R.K., Y.M.K and J.T.L. interpreted the data and validated the results. J.T.L. and M.S.K. drafted the manuscript. All authors critically revised the manuscript and approved the final version. Funding: This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (RS-2024-00336696, RS-2026-25469859). Data availability: The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author. Competing interests : The authors declare no competing interests. Consent for publication: Not applicable. Ethics approval and consent to participate: This retrospective cohort study was approved by the Institutional Review Board (IRB) of Korea University Anam Hospital (IRB No: 2022AN0110). The requirement for informed consent was waived by the IRB due to the retrospective design and use of de-identified data. All procedures were conducted in accordance with the Declaration of Helsinki. Clinical trial number Not applicable. References Price AJ, Alvand A, Troelsen A, Katz JN, Hooper G, Gray A, et al. Knee replacement. Lancet. 2018;392:1672–82. https://doi.org/10.1016/s0140-6736(18)32344-4 . Duong V, Oo WM, Ding C, Culvenor AG, Hunter DJ. Evaluation and Treatment of Knee Pain: A Review. JAMA. 2023;330:1568–80. https://doi.org/10.1001/jama.2023.19675 . Katz JN, Arant KR, Loeser RF. Diagnosis and Treatment of Hip and Knee Osteoarthritis: A Review. JAMA. 2021;325:568–78. https://doi.org/10.1001/jama.2020.22171 . Orange GM, Hince DA, Travers MJ, Stanton TR, Jones M, Sharma S, et al. Physical Function Following Total Knee Arthroplasty for Osteoarthritis: A Longitudinal Systematic Review With Meta-analysis. J Orthop Sports Phys Ther. 2025;55:1–11. https://doi.org/10.2519/jospt.2024.12570 . Hylkema TH, Brouwer S, Stewart RE, van Beveren J, Rijk PC, Brouwer RW, et al. Two-year recovery courses of physical and mental impairments, activity limitations, and participation restrictions after total knee arthroplasty among working-age patients. Disabil Rehabil. 2022;44:291–300. https://doi.org/10.1080/09638288.2020.1766583 . Wylde V, Penfold C, Rose A, Blom AW. Variability in long-term pain and function trajectories after total knee replacement: A cohort study. Orthop Traumatol Surg Res. 2019;105:1345–50. https://doi.org/10.1016/j.otsr.2019.08.014 . Connelly JW, Galea VP, Rojanasopondist P, Nielsen CS, Bragdon CR, Kappel A, et al. Which Preoperative Factors are Associated with Not Attaining Acceptable Levels of Pain and Function After TKA? Findings from an International Multicenter Study. Clin Orthop Relat Res. 2020;478:1019–28. https://doi.org/10.1097/corr.0000000000001162 . Nanjo K, Ikeda T, Nagashio N, Sakai T, Jinno T. Preoperative Predictors of Instrumental Activities of Daily Living Disability in Older Adults 6 Months After Knee Arthroplasty. Arch Phys Med Rehabil. 2023;104:1260–7. https://doi.org/10.1016/j.apmr.2023.02.007 . Hewlett-Smith N, Pope R, Furness J, Simas V, Hing W. Prognostic factors for inpatient functional recovery following total hip and knee arthroplasty: a systematic review. Acta Orthop. 2020;91:313–8. https://doi.org/10.1080/17453674.2020.1744852 . Katzman JL, Cardillo C, Schaffler BC, Schwarzkopf R, Rozell JC. Can Preoperative Patient-Reported Outcome Measures Predict Clinical Outcomes Following Total Knee Arthroplasty? J Arthroplasty. 2026;41:1064–74. https://doi.org/10.1016/j.arth.2025.08.075 . Elfanagely O, Toyoda Y, Othman S, Mellia JA, Basta M, Liu T, et al. Machine Learning and Surgical Outcomes Prediction: A Systematic Review. J Surg Res. 2021;264:346–61. https://doi.org/10.1016/j.jss.2021.02.045 . Ogink PT, Groot OQ, Karhade AV, Bongers MER, Oner FC, Verlaan JJ, et al. Wide range of applications for machine-learning prediction models in orthopedic surgical outcome: a systematic review. Acta Orthop. 2021;92:526–31. https://doi.org/10.1080/17453674.2021.1932928 . Karimi AH, Langberg J, Malige A, Rahman O, Abboud JA, Stone MA. Accuracy of machine learning to predict the outcomes of shoulder arthroplasty: a systematic review. Arthroplasty. 2024;6:26. https://doi.org/10.1186/s42836-024-00244-4 . Aydin M, Orhan F. Evaluating the Impact of AI in Orthopedics: A Quantitative Analysis of Advancements and Challenges. Bratislava Med J. 2025;126:1858–75. https://doi.org/10.1007/s44411-025-00170-0 . Ciobanu-Caraus O, Aicher A, Kernbach JM, Regli L, Serra C, Staartjes VE. A critical moment in machine learning in medicine: on reproducible and interpretable learning. Acta Neurochir (Wien). 2024;166:14. https://doi.org/10.1007/s00701-024-05892-8 . Adadi A, Berrada M. Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI). IEEE Access. 2018;6:52138–60. https://doi.org/10.1109/access.2018.2870052 . Rudin C. Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead. Nat Mach Intell. 2019;1:206–15. https://doi.org/10.1038/s42256-019-0048-x . Ribeiro MT, Singh S, Guestrin C. Association for Computing Machinery. Why Should I Trust You? Explaining the Predictions of Any Classifier. 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD). San Francisco, CA2016. pp. 1135–44. Mienye ID, Obaido G, Jere N, Mienye E, Aruleba K, Emmanuel ID, et al. A survey of explainable artificial intelligence in healthcare: Concepts, applications, and challenges. Inf Med Unlocked. 2024;51:101587. https://doi.org/https://doi.org/10.1016/j.imu.2024.101587 . Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25:44–56. https://doi.org/10.1038/s41591-018-0300-7 . Wang B, Liu SM, Zhao YS, IEEE. Research on Modeling and Application of Magneto-Optical Measurement Technology. 3rd IEEE Student Conference on Electrical Machines and Systems (SCEMS). Jinan, PEOPLES R CHINA2020. pp. 207–11. Bellamy N, Buchanan WW, Goldsmith CH, Campbell J, Stitt LW. Validation study of WOMAC: a health status instrument for measuring clinically important patient relevant outcomes to antirheumatic drug therapy in patients with osteoarthritis of the hip or knee. J Rheumatol. 1988;15:1833–40. White DK, Master H. Patient-Reported Measures of Physical Function in Knee Osteoarthritis. Rheum Dis Clin North Am. 2016;42:239–52. https://doi.org/10.1016/j.rdc.2016.01.005 . Woolacott NF, Corbett MS, Rice SJ. The use and reporting of WOMAC in the assessment of the benefit of physical therapies for the pain of osteoarthritis of the knee: findings from a systematic review of clinical trials. Rheumatology (Oxford). 2012;51:1440–6. https://doi.org/10.1093/rheumatology/kes043 . Angst F, Ewert T, Lehmann S, Aeschlimann A, Stucki G. The factor subdimensions of the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) help to specify hip and knee osteoarthritis. a prospective evaluation and validation study. J Rheumatol. 2005;32:1324–30. Ebrahimzadeh MH, Makhmalbaf H, Birjandinejad A, Keshtan FG, Hoseini HA, Mazloumi SM. The Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) in Persian Speaking Patients with Knee Osteoarthritis. Arch Bone Jt Surg. 2014;2:57–62. McConnell S, Kolopack P, Davis AM. The Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC): a review of its utility and measurement properties. Arthritis Rheum. 2001;45:453–61. https://doi.org/10.1002/1529-0131(200110)45:5%3C453::aid-art365%3E3.0.co;2-w . Huskisson EC. Measurement of pain. Lancet. 1974;2:1127–31. https://doi.org/10.1016/s0140-6736(74)90884-8 . Bijur PE, Silver W, Gallagher EJ. Reliability of the visual analog scale for measurement of acute pain. Acad Emerg Med. 2001;8:1153–7. https://doi.org/10.1111/j.1553-2712.2001.tb01132.x . EuroQol Group. EuroQol–a new facility for the measurement of health-related quality of life. Health Policy (New York). 1990;16:199–208. https://doi.org/10.1016/0168-8510(90)90421-9 . Rabin R, de Charro F. EQ-5D: a measure of health status from the EuroQol Group. Ann Med. 2001;33:337–43. https://doi.org/10.3109/07853890109002087 . Conner-Spady BL, Marshall DA, Bohm E, Dunbar MJ, Noseworthy TW. Comparing the validity and responsiveness of the EQ-5D-5L to the Oxford hip and knee scores and SF-12 in osteoarthritis patients 1 year following total joint replacement. Qual Life Res. 2018;27:1311–22. https://doi.org/10.1007/s11136-018-1808-5 . Bilbao A, García-Pérez L, Arenaza JC, García I, Ariza-Cardiel G, Trujillo-Martín E, et al. Psychometric properties of the EQ-5D-5L in patients with hip or knee osteoarthritis: reliability, validity and responsiveness. Qual Life Res. 2018;27:2897–908. https://doi.org/10.1007/s11136-018-1929-x . Podsiadlo D, Richardson S. The timed Up & Go: a test of basic functional mobility for frail elderly persons. J Am Geriatr Soc. 1991;39:142–8. https://doi.org/10.1111/j.1532-5415.1991.tb01616.x . Onodera CMK, Coelho-Júnior HJ, Sampaio RAC, Santos Duarte Lana JF, Teixeira LFM, Uchida MC, et al. The importance of objectively measuring functional tests in complement to self-report assessments in patients with knee osteoarthritis. Gait Posture. 2020;82:33–7. https://doi.org/10.1016/j.gaitpost.2020.08.121 . Nightingale CJ, Mitchell SN, Butterfield SA. Validation of the Timed Up and Go Test for Assessing Balance Variables in Adults Aged 65 and Older. J Aging Phys Act. 2019;27:230–3. https://doi.org/10.1123/japa.2018-0049 . Zasadzka E, Borowicz AM, Roszak M, Pawlaczyk M. Assessment of the risk of falling with the use of timed up and go test in the elderly with lower extremity osteoarthritis. Clin Interv Aging. 2015;10:1289–98. https://doi.org/10.2147/cia.S86001 . ATS Committee on Proficiency Standards for Clinical Pulmonary Function Laboratories. ATS statement: guidelines for the six-minute walk test. Am J Respir Crit Care Med. 2002;166:111–7. https://doi.org/10.1164/ajrccm.166.1.at1102 . Gacto-Sánchez M, Lozano-Meca JA, Lozano-Guadalajara JV, Montilla-Herrador J. Concurrent validity of the 2-and 6-minute walk test in knee osteoarthritis. Knee. 2023;43:34–41. https://doi.org/10.1016/j.knee.2023.05.009 . Ozsoy G, Kodak MI, Kararti C, Ozyurt F, Kodak SB, Karacay BC, et al. Optimal cut-off scores of performance-based tests of physical function to discriminate disease severity in patients with knee osteoarthritis. Knee. 2025;55:179–85. https://doi.org/10.1016/j.knee.2025.04.019 . Ramalho RB, Casonato NA, Montilha VB, Chaves TC, Mattiello SM, Selistre LFA. Construct Validity and Responsiveness of Performance-based Tests in Individuals With Knee Osteoarthritis. Arch Phys Med Rehabil. 2024;105:1862–9. https://doi.org/10.1016/j.apmr.2024.05.024 . Sharma S, Wilson R, Pryymachenko Y, Pathak A, Chua J, Gwynne-Jones D, et al. Reliability, Validity, Responsiveness, and Minimum Important Change of the Stair Climb Test in Adults With Hip and Knee Osteoarthritis. Arthritis Care Res (Hoboken). 2023;75:1147–57. https://doi.org/10.1002/acr.24821 . Sørensen B, Aagaard P, Couppé C, Suetta C, Johannsen FE, Magnusson SP. Stair climb muscle power is associated with gait speed, sit-to-stand performance, patient-reported outcomes and objective measures of mechanical muscle function in individuals with knee osteoarthritis - secondary analysis from an RCT. Musculoskelet Sci Pract. 2025;77:103332. https://doi.org/10.1016/j.msksp.2025.103332 . Casaña J, Calatayud J, Silvestre A, Sánchez-Frutos J, Andersen LL, Jakobsen MD, et al. Knee Extensor Muscle Strength Is More Important Than Postural Balance for Stair-Climbing Ability in Elderly Patients with Severe Knee Osteoarthritis. Int J Environ Res Public Health. 2021;18:3637. https://doi.org/10.3390/ijerph18073637 . Hammami N, Bouzouraa E, Ölmez C, Hattabi S, Mhimdi N, Khezami MA, et al. Isokinetic Knee Strengthening Impact on Physical and Functional Performance, Pain Tolerance, and Quality of Life in Overweight/Obese Women with Patellofemoral Pain Syndrome. J Clin Med. 2024;13:4696. https://doi.org/10.3390/jcm13164696 . Berghmans DDP, Lenssen AF, Emans PJ, de Bie RA. Functions, disabilities and perceived health in the first year after total knee arthroplasty; a prospective cohort study. BMC Musculoskelet Disord. 2018;19:250. https://doi.org/10.1186/s12891-018-2159-7 . Christensen JC, Capin JJ, Hinrichs LA, Aljehani M, Stevens-Lapsley JE, Zeni JA. Gait mechanics are influenced by quadriceps strength, age, and sex after total knee arthroplasty. J Orthop Res. 2021;39:1523–32. https://doi.org/10.1002/jor.24878 . Choi JH, Kim BR, Kim SR, Nam KW, Lee SY, Suh MJ. Performance-based physical function correlates with walking speed and distance at 3 months post unilateral total knee arthroplasty. Gait Posture. 2021;87:163–9. https://doi.org/10.1016/j.gaitpost.2021.04.041 . Suh MJ, Kim BR, Kim SR, Han EY, Nam KW, Lee SY, et al. Bilateral Quadriceps Muscle Strength and Pain Correlate With Gait Speed and Gait Endurance Early After Unilateral Total Knee Arthroplasty: A Cross-sectional Study. Am J Phys Med Rehabil. 2019;98:897–905. https://doi.org/10.1097/phm.0000000000001222 . Additional Declarations No competing interests reported. Supplementary Files Supplementaryfiles20260413.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 11 May, 2026 Reviews received at journal 10 May, 2026 Reviews received at journal 10 May, 2026 Reviewers agreed at journal 06 May, 2026 Reviewers agreed at journal 05 May, 2026 Reviewers agreed at journal 05 May, 2026 Reviews received at journal 05 May, 2026 Reviewers agreed at journal 05 May, 2026 Reviews received at journal 04 May, 2026 Reviewers agreed at journal 04 May, 2026 Reviewers agreed at journal 04 May, 2026 Reviewers agreed at journal 04 May, 2026 Reviewers invited by journal 04 May, 2026 Editor assigned by journal 16 Apr, 2026 Submission checks completed at journal 16 Apr, 2026 First submitted to journal 13 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9408042","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":638028644,"identity":"2ce97cc4-4a73-4d33-8629-db8b20c185b8","order_by":0,"name":"Jin Taek Lee","email":"","orcid":"","institution":"Korea University","correspondingAuthor":false,"prefix":"","firstName":"Jin","middleName":"Taek","lastName":"Lee","suffix":""},{"id":638028646,"identity":"e329afa2-e684-4b81-928e-4b0cc01233d2","order_by":1,"name":"Min Sun Kim","email":"","orcid":"","institution":"Hansung University","correspondingAuthor":false,"prefix":"","firstName":"Min","middleName":"Sun","lastName":"Kim","suffix":""},{"id":638028648,"identity":"7593379e-7a6c-4ecc-9ee0-968562c59d16","order_by":2,"name":"Young Mo Kim","email":"","orcid":"","institution":"Korea University","correspondingAuthor":false,"prefix":"","firstName":"Young","middleName":"Mo","lastName":"Kim","suffix":""},{"id":638028649,"identity":"74fce827-4b71-4c47-8af0-78d01fd3aa20","order_by":3,"name":"Jun Hwan Choi","email":"","orcid":"","institution":"Jeju National University","correspondingAuthor":false,"prefix":"","firstName":"Jun","middleName":"Hwan","lastName":"Choi","suffix":""},{"id":638028650,"identity":"78df4307-c570-4e7c-b5aa-5a40f6f91dd9","order_by":4,"name":"Bo Ryun Kim","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2ElEQVRIie3PMQrCMBTG8U8KcUlxDVTsFVI6iFA8S0WIS7sXHHRKl+qsCJ6lGGiXegA33V3ERcFBQXBwSOvmkP/44Md7DzCZ/jAOghw86LHPiDYiifB/I0ClRqvGpO8Uo91Bisk6XZyONwxd0P1RSwZLkatYBvGGlr6XYezN7ZTrD6va8xcR8ZYJwiisEB1S88ubqEmPifb9gVkTQnIVVSp0XlssChXClnoyyEiookR466ywnC4vPUkLPelT4l8jHrislK3LOZm6HSr05PtOoOYTk8lkMjXpCZQtPnx3XdK9AAAAAElFTkSuQmCC","orcid":"","institution":"Korea University","correspondingAuthor":true,"prefix":"","firstName":"Bo","middleName":"Ryun","lastName":"Kim","suffix":""},{"id":638028653,"identity":"fee3ff37-3f61-4c21-b656-c15ca7706031","order_by":5,"name":"Seoung-Ho Choi","email":"","orcid":"","institution":"Hansung University","correspondingAuthor":false,"prefix":"","firstName":"Seoung-Ho","middleName":"","lastName":"Choi","suffix":""}],"badges":[],"createdAt":"2026-04-13 20:08:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9408042/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9408042/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109119049,"identity":"b2b7f561-4d9a-432a-accf-25857a77129e","added_by":"auto","created_at":"2026-05-12 16:56:19","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":555370,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation analysis between independent and dependent variables using CCA. Only correlations with \u003cem\u003ep\u003c/em\u003e ≤ 0.001 and statistically significant r scores are presented. In all panels, the horizontal axis represents preoperative values, and the vertical axis represents the corresponding 3-month postoperative values. (a) Preoperative SCT ascent performance and 3-month postoperative 6MWT, (b) Preoperative SCT descent performance and 3-month postoperative 6MWT, (c) Preoperative Knee Ex PT(BW) and 3-month postoperative Knee Ex PT(BW), (d) Sex and 3-month postoperative Knee Ex PT(BW). 6MWT, 6-minute walk test; SCT, stair climbing test; Knee Ex PT(BW), knee extension peak torque normalized to body weight; CCA, canonical correlation analysis.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9408042/v1/e334a4cf56dafc4be7c5df2b.png"},{"id":109119031,"identity":"7d61f800-6f62-4407-8a5e-06b91ac5ac51","added_by":"auto","created_at":"2026-05-12 16:56:07","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":127060,"visible":true,"origin":"","legend":"\u003cp\u003eResidual plots for predicting functional outcomes 3 months after TKA (a) 6MWT, (b) Knee Ex PT(BW). 6MWT, 6-minute walk test; Knee Ex PT(BW), knee extension peak torque normalized to body weight; TKA, total knee arthroplasty.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9408042/v1/8b3d177905346f5b298cc345.png"},{"id":109119035,"identity":"48356ee9-aa59-4cdf-ab72-ea8e13e500ac","added_by":"auto","created_at":"2026-05-12 16:56:08","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":154456,"visible":true,"origin":"","legend":"\u003cp\u003eQ-Q plot for predicting functional outcomes 3 months after TKA (a) 6MWT, (b) Knee Ex PT(BW). 6MWT, 6-minute walk test; Knee Ex PT(BW), knee extension peak torque normalized to body weight; TKA, total knee arthroplasty.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-9408042/v1/349920ca466abae5476349f0.png"},{"id":109119058,"identity":"0caf8dd0-02b3-4a82-890c-1a76502cc357","added_by":"auto","created_at":"2026-05-12 16:56:21","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":141823,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of model-predicted versus actual values for predicting functional outcomes 3 months after TKA, (a) 6MWT, (b) Knee Ex PT(BW). 6MWT, 6-minute walk test; Knee Ex PT(BW), knee extension peak torque normalized to body weight; TKA, total knee arthroplasty.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-9408042/v1/a28c83baf108015041c61651.png"},{"id":109119033,"identity":"7678866b-1567-4048-8453-0a3daebcb23f","added_by":"auto","created_at":"2026-05-12 16:56:08","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":139591,"visible":true,"origin":"","legend":"\u003cp\u003eFeature importance of models predicting functional outcomes at 3 months post-TKA. Each panel corresponds to a specific outcome: (a) 6MWT (3M), (b) Knee Ex PT(BW) (3M).\u003c/p\u003e\n\u003cp\u003eWOMAC, The Western Ontario and McMaster Universities Osteoarthritis Index; VAS, visual analog scale; EQ-5D, EuroQol-5 dimension Questionnaires; TUG, timed up and go test; 6MWT, 6-minute walk test; SCT, stair climbing test; Knee Ex PT(BW), knee extension peak torque normalized to body weight; Knee Fl PT(BW), knee flexor peak torque normalized to body weight; TKA, total knee arthroplasty; BMI, body mass index; SMI, skeletal muscle index; CI, confidence interval.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-9408042/v1/71bed8fd9b80d609a04a1381.png"},{"id":109119067,"identity":"7e4df87e-1860-48a7-9b63-105ec15491bc","added_by":"auto","created_at":"2026-05-12 16:56:26","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":160246,"visible":true,"origin":"","legend":"\u003cp\u003eModel performance for predicting functional outcomes on external data assessed using LIME analysis. Each panel corresponds to a specific outcome: (a) 6MWT (3M), (b) Knee Ex PT (BW) (3M). WOMAC, The Western Ontario and McMaster Universities Osteoarthritis Index; VAS, visual analog scale; EQ-5D, EuroQol-5 dimension Questionnaires; TUG, timed up and go test; 6MWT, 6-minute walk test; SCT, stair climbing test; Knee Ex PT(BW), knee extension peak torque normalized to body weight; Knee Fl PT(BW), knee flexor peak torque normalized to body weight; TKA, total knee arthroplasty; BMI, body mass index; SMI, skeletal muscle index; CI, confidence interval; LIME, Local Interpretable Model-agnostic Explanations.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-9408042/v1/60e02b0294673eabda363b34.png"},{"id":109119205,"identity":"f9d6625b-8df7-4645-8ba1-0955d160b5ce","added_by":"auto","created_at":"2026-05-12 16:56:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1645599,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9408042/v1/c0edcf3c-b7b1-4751-b247-3395099c3cba.pdf"},{"id":109119027,"identity":"8c588bd0-fddb-4937-9d7f-69699a7b7cb0","added_by":"auto","created_at":"2026-05-12 16:56:07","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":36725,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfiles20260413.docx","url":"https://assets-eu.researchsquare.com/files/rs-9408042/v1/402b1b793681e780730e86cc.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development and validation of machine learning models for predicting functional outcomes following total knee arthroplasty","fulltext":[{"header":"Introduction","content":"\u003cp\u003eTotal knee arthroplasty (TKA) is among the most commonly performed surgical procedures for alleviating pain and restoring function in patients with end-stage knee osteoarthritis [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Despite standardized surgical techniques and postoperative care, substantial interindividual variability exists in recovery trajectories, with marked differences in functional improvement, walking ability, and muscle strength [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Such variability has significant implications for activities of daily living, quality of life, and healthcare utilization [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In clinical practice, a subset of patients experience delayed functional recovery, persistent pain, or prolonged rehabilitation, underscoring the need for early identification of individuals at risk for suboptimal outcomes [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Accurate preoperative prognostic assessment could facilitate personalized rehabilitation strategies and more efficient allocation of healthcare resources [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. However, postoperative recovery following TKA is influenced by complex interactions among demographic characteristics, baseline physical function, pain severity, muscle strength, and psychosocial factors [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eConventional prognostic approaches primarily rely on isolated clinical indicators\u0026mdash;such as pain scores, range of motion, or functional tests\u0026mdash;which fail to capture the multidimensional and nonlinear nature of postoperative recovery [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Furthermore, traditional statistical models often demonstrate limited explanatory power when applied to heterogeneous clinical populations, restricting their utility for individualized decision-making [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMachine learning (ML) techniques offer a promising alternative by enabling the integration of high-dimensional clinical, functional, and patient-reported data while modeling complex nonlinear relationships [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Recent studies have demonstrated the potential of ML-based models to improve outcome prediction after orthopedic surgery [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. However, the \u0026ldquo;black-box\u0026rdquo; nature of many ML algorithms poses a significant barrier to clinical adoption, as clinicians require transparent and interpretable decision-support tools [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Explainable artificial intelligence methods have been developed to address this limitation [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Among these, Local Interpretable Model-agnostic Explanations (LIME) provide case-level interpretability by identifying features that contribute most strongly to individual predictions [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. LIME\u0026rsquo;s low computational burden and local explanatory framework make it particularly suitable for time-sensitive clinical settings, such as perioperative rehabilitation planning [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAccordingly, the primary aim of this study was to develop and validate an explainable ML-based framework that integrates preoperative clinical, functional, lifestyle, and psychological variables to predict functional recovery at 3 months following TKA. Particular emphasis was placed on objectively measurable functional domains, including walking endurance and knee extensor strength. A secondary aim was to apply LIME to visually and quantitatively identify key predictors influencing individual patient outcomes, thereby facilitating early risk stratification and personalized rehabilitation planning.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and participants\u003c/h2\u003e \u003cp\u003eThis retrospective cohort study included consecutive patients who underwent TKA for end-stage knee osteoarthritis at OO University Hospital between September 2013 and January 2022. The study protocol was approved by the Institutional Review Board of OO University OO Hospital (IRB No. 2022AN0110), and the requirement for informed consent was waived due to the retrospective nature of the study.\u003c/p\u003e \u003cp\u003eInclusion and exclusion criteria\u003c/p\u003e \u003cp\u003ePatients were eligible if they met the following criteria:\u003c/p\u003e \u003cp\u003e(1) Diagnosis of end-stage knee osteoarthritis (Kellgren\u0026ndash;Lawrence grade 3 or 4);\u003c/p\u003e \u003cp\u003e(2) Completion of standardized preoperative baseline assessments;\u003c/p\u003e \u003cp\u003e(3) Availability of postoperative functional evaluations at 3 months postoperatively; and\u003c/p\u003e \u003cp\u003e(4) Ability to ambulate independently, with or without assistive devices.\u003c/p\u003e \u003cp\u003eExclusion criteria were as follows:\u003c/p\u003e \u003cp\u003e(1) History of neurological or orthopedic disorders affecting gait or lower-limb function;\u003c/p\u003e \u003cp\u003e(2) Missing essential preoperative predictor variables; or\u003c/p\u003e \u003cp\u003e(3) Loss to follow-up before the 3-month postoperative assessment.\u003c/p\u003e \u003cp\u003eA total of 313 patients met the eligibility criteria and were included in the final analysis. A flow diagram summarizing the patient selection process is provided in Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eBaseline multidimensional assessments\u003c/h3\u003e\n\u003cp\u003ePatient-reported assessments\u003c/p\u003e\n\u003ch3\u003eThe Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC)\u003c/h3\u003e\n\u003cp\u003eThe WOMAC questionnaire is a disease-specific, self-administered instrument designed to assess knee and hip osteoarthritis. WOMAC evaluates pain, stiffness, and physical function [\u003cspan additionalcitationids=\"CR23 CR24 CR25 CR26\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] using 24 items (5 pain, 2 stiffness, and 17 function items), each rated on a 5-point Likert scale (0\u0026thinsp;=\u0026thinsp;none to 4\u0026thinsp;=\u0026thinsp;extreme). Higher scores indicate more severe symptoms and functional limitations. This tool captures the multidimensional impact of osteoarthritis on daily activities.\u003c/p\u003e\n\u003ch3\u003eVisual analog scale (VAS)\u003c/h3\u003e\n\u003cp\u003eKnee pain intensity was measured using a 10-cm horizontal VAS, anchored by \u0026ldquo;no pain\u0026rdquo; (0) and \u0026ldquo;worst possible pain\u0026rdquo; (10) [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Patients were instructed to indicate their resting pain level. The VAS provides a simple and sensitive measure of perceived pain severity that is widely used in clinical and research settings [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eEuroQol-5 Dimension Questionnaire (EQ-5D)\u003c/h3\u003e\n\u003cp\u003eThe EQ-5D is a standardized measure of general health-related quality of life [\u003cspan additionalcitationids=\"CR31 CR32\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], assessing five domains: mobility, self-care, usual activities, pain/discomfort, and anxiety/depression. Each domain is rated at three levels (no problems, some problems, or extreme problems). Responses are converted into a single index score that reflects overall health utility.\u003c/p\u003e \u003cp\u003ePerformance-based functional assessments\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eTimed Up and Go (TUG) test\u003c/h2\u003e \u003cp\u003eThe TUG test measures the time (seconds) required for a patient to rise from a chair, walk 3 m, turn, return, and sit down again [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Longer times reflect poorer balance and mobility. TUG provides a quick, reliable indicator of overall functional mobility and fall risk in older adults [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Each patient sat with their back against a standardized chair (seat height, 44 cm; depth, 45 cm; width, 49 cm; armrest height, 64 cm) positioned at the end of a 3-m walkway. On the command \u0026ldquo;go,\u0026rdquo; patients stood, walked at a comfortable pace to the 3-m mark, turned, walked back, and sat down again without physical assistance while being timed.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSix-minute walk test (6MWT)\u003c/h3\u003e\n\u003cp\u003eThe 6MWT measures the distance (m) a patient can walk in 6 minutes along a marked hallway. It evaluates aerobic capacity, endurance, and walking function by integrating cardiopulmonary fitness and lower-limb performance [\u003cspan additionalcitationids=\"CR39\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. A greater distance indicates better overall locomotor ability. Patients were instructed to walk as far as possible for 6 minutes along a 50-m hallway marked with lines.\u003c/p\u003e\n\u003ch3\u003eStair Climbing Test (SCT)\u003c/h3\u003e\n\u003cp\u003eThe SCT evaluates functional mobility and lower-limb power by measuring the time required to ascend and descend a 12-step staircase (17-cm step height, 25-cm width). Shorter completion times reflect greater concentric and eccentric muscle performance [\u003cspan additionalcitationids=\"CR42\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. This test reflects the capacity to perform daily mobility tasks that require high muscle power, such as stair negotiation.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eIsometric strength of the knee extensors and flexors (Knee Ex PT(BW) and Knee Fl PT(BW))\u003c/h2\u003e \u003cp\u003eMaximal isometric torque of the bilateral knee extensors and flexors was measured using an isokinetic dynamometer (Computer Sports Medicine Inc., Stoughton, MA, USA). Before the test, patients performed light stretching to relax the muscles. During the procedure, patients were instructed to grasp the sidebars of the apparatus. Following a standardized warm-up, the knee joint was fixed at 60\u0026deg; of flexion (to generate maximal isometric force), and patients performed maximal voluntary contractions until the torque failed to increase by \u0026gt;\u0026thinsp;5% across three successive attempts. These measures provide an objective index of quadriceps and hamstring capacity, which are critical determinants of postoperative recovery [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNine postoperative functional and patient-reported outcomes were assessed at baseline and at 3 months after surgery, including pain, mobility, quality of life, walking endurance, and knee extensor strength.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eData preprocessing\u003c/h2\u003e \u003cp\u003eAll variables were examined for completeness and consistency. Categorical variables were numerically encoded. Missing values were imputed using clinically informed encoding strategies (e.g., absence of documented comorbidities), while continuous variables were imputed using simple imputation methods prior to standardization. Continuous variables were then standardized to ensure compatibility with ML algorithms. The dataset comprised 313 samples and was randomly partitioned into a training set (n\u0026thinsp;=\u0026thinsp;188), an internal validation set (n\u0026thinsp;=\u0026thinsp;63), and an external validation test set (n\u0026thinsp;=\u0026thinsp;63). The external test set was randomly held out from the same cohort.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eML modeling and evaluation\u003c/h2\u003e \u003cp\u003eML\u0026ndash;based regression models were developed to predict functional recovery at 3 months following TKA. All outcomes were initially considered as potential prediction targets. An exploratory screening process identified outcomes demonstrating stable predictive performance. Outcomes demonstrating reproducible explanatory power and consistent performance across folds were retained for subsequent structural and interpretability analyses.\u003c/p\u003e \u003cp\u003eSeven ML regressors were used: AdaBoost Regressor, Extra Trees Regressor, Gradient Boosting Regressor, Random Forest Regressor, Histogram-based Gradient Boosting Regressor, Support Vector Machine Regressor, and Tabular Prior-data Fitted Network (TabPFN) Regressor. Model training and hyperparameter optimization were performed using nested cross-validation, with five outer folds for performance evaluation and five inner folds for grid-search\u0026ndash;based hyperparameter tuning (Supplementary Table\u0026nbsp;1).\u003c/p\u003e \u003cp\u003eModel performance was assessed using mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), and the coefficient of determination (R\u0026sup2;). For each outcome, the model achieving the highest mean R\u0026sup2; across the outer validation folds was selected as the final predictive model. To enhance clinical interpretability, LIME was applied to the selected models [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. LIME was used to identify key features contributing to individual predictions. Based on LIME rankings and clinical relevance, clinicians selected 4\u0026ndash;6 representative features for interpretive analysis, integrating highly ranked model-derived features with additional clinically important variables.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eDescriptive statistics were used to summarize baseline characteristics. Independent sample \u003cem\u003et\u003c/em\u003e-tests, chi-square tests, and canonical correlation analyses (CCA) were used to evaluate relationships between individual features and clinical outcomes. Odds ratios (ORs) were calculated to quantify the intensity of associations between categorical variables and clinical outcomes. Univariate associations were evaluated using Pearson\u0026rsquo;s correlation coefficients for continuous variables, and \u003cem\u003et\u003c/em\u003e-tests or chi-square tests for group comparisons, as appropriate. Variables with relevant univariable associations were included in the multivariable linear regression models. Statistical significance was defined as \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Where external validation was available, both internally cross-validated performance and external validation results were reported.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eCohort characteristics\u003c/h2\u003e \u003cp\u003eThe final analytic cohort comprised 313 patients (mean age, 71.8\u0026thinsp;\u0026plusmn;\u0026thinsp;5.9 years), including 49 males (15.7%) and 264 females (84.3%). Baseline demographic characteristics, comorbidities, and preoperative functional and patient-reported assessments are summarized in Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The prevalence of major comorbidities included hypertension (65.5%), diabetes mellitus (17.6%), hyperlipidemia (25.6%), cardiovascular disease (5.4%), and prior stroke (1.6%).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic and clinical characteristics of the patients (N\u0026thinsp;=\u0026thinsp;313)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eValues\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71.81\u0026thinsp;\u0026plusmn;\u0026thinsp;5.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex, male/female (number)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49 (15.7%) / 264 (84.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e153\u0026thinsp;\u0026plusmn;\u0026thinsp;7.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62.73\u0026thinsp;\u0026plusmn;\u0026thinsp;9.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody mass index (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.53\u0026thinsp;\u0026plusmn;\u0026thinsp;3.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSkeletal muscle index (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical characteristics (number)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eK\u0026ndash;L grade 2 / 3 / 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (0.3%) / 51 (16.3%) / 261 (83.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTKA surgery history\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e168 (53.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStroke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (1.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiovascular disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (5.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e205 (65.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes mellitus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55 (17.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHyperlipidemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80 (25.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003eK\u0026ndash;L, Kellgren\u0026ndash;Lawrence; TKA, total knee arthroplasty\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFunctional and patient-reported assessments of patients: baseline and 3 months after TKA (N\u0026thinsp;=\u0026thinsp;313)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBaseline\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3-month follow-up\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean difference\u003c/p\u003e \u003cp\u003e(pre-post)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003et-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWOMAC total\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e42.88\u0026thinsp;\u0026plusmn;\u0026thinsp;10.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e26.97\u0026thinsp;\u0026plusmn;\u0026thinsp;9.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e25.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVAS score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e6.82\u0026thinsp;\u0026plusmn;\u0026thinsp;1.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e2.40\u0026thinsp;\u0026plusmn;\u0026thinsp;1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e39.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEQ-5D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.59\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.80\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;26.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTUG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e11.88\u0026thinsp;\u0026plusmn;\u0026thinsp;3.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e8.98\u0026thinsp;\u0026plusmn;\u0026thinsp;1.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6MWT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e309.77\u0026thinsp;\u0026plusmn;\u0026thinsp;158.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e458.61\u0026thinsp;\u0026plusmn;\u0026thinsp;94.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;148.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;15.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCT ascent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e14.73\u0026thinsp;\u0026plusmn;\u0026thinsp;6.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e10.48\u0026thinsp;\u0026plusmn;\u0026thinsp;3.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCT descent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e17.36\u0026thinsp;\u0026plusmn;\u0026thinsp;6.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e12.19\u0026thinsp;\u0026plusmn;\u0026thinsp;4.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKnee Ex PT(BW)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e79.25\u0026thinsp;\u0026plusmn;\u0026thinsp;27.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e84.67\u0026thinsp;\u0026plusmn;\u0026thinsp;23.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;5.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;3.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKnee Fl PT(BW)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e47.89\u0026thinsp;\u0026plusmn;\u0026thinsp;15.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e52.40\u0026thinsp;\u0026plusmn;\u0026thinsp;13.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;4.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;5.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWOMAC, The Western Ontario and McMaster Universities Osteoarthritis Index; VAS, visual analog scale; EQ-5D, EuroQol-5 dimension Questionnaires; TUG, timed up and go test; 6MWT, 6-minute walk test; SCT, stair climbing test; Knee Ex PT(BW), knee extension peak torque normalized to body weight; Knee Fl PT(BW), knee flexor peak torque normalized to body weight; TKA, total knee arthroplasty.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eBaseline-to-3-month changes in functional outcomes: univariate analysis prior to ML modeling\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents univariable analyses of associations between preoperative and 3-month postoperative functional outcomes following TKA. All functional and patient-reported outcome measures demonstrated statistically significant improvements at 3 months (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.001), indicating global recovery in pain, mobility, walking endurance, stair performance, and muscle strength. Patient-reported outcomes, including the WOMAC total score, VAS pain score, and EQ-5D index, showed marked postoperative improvement, reflecting substantial reductions in pain and perceived functional limitations. Performance-based measures, such as the TUG test, SCT (ascent and descent), and 6MWT, also demonstrated significant gains, indicating enhanced mobility and functional capacity during the early postoperative period. Additionally, both knee extensor peak torque normalized to body weight (Knee Ex PT(BW)) and flexor peak torque normalized to body weight (Knee Fl PT(BW)) significantly increased at 3 months, reflecting recovery of lower-limb muscle strength following surgery.\u003c/p\u003e \u003cp\u003eThese univariate pre\u0026ndash;post comparisons were performed to characterize overall patterns of early postoperative recovery rather than to identify predictive targets. Although all assessed variables demonstrated statistically significant improvement, statistical significance alone does not imply suitability for outcome prediction. Therefore, subsequent analyses focused on identifying functional outcomes with meaningful predictive potential when multiple preoperative factors were considered simultaneously using ML approaches.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eModel performance and stability\u0026ndash;guided outcome selection\u003c/h2\u003e \u003cp\u003eAll nine postoperative functional and patient-reported assessments were initially evaluated as potential prediction targets using ML regression models. Although several outcomes demonstrated statistically significant model correlations, exploratory analyses revealed that most patient-reported and mobility-based outcomes showed limited predictive performance. Specifically, they exhibited low R\u0026sup2; values, unstable validation results, and heterogeneous residual distributions across models, limiting their suitability as reliable prediction targets.\u003c/p\u003e \u003cp\u003eIn contrast, the 6MWT and Knee Ex PT(BW) demonstrated comparatively greater explanatory power, more reproducible performance across ML algorithms, and greater residual stability. For the 6MWT, the support vector machine regressor achieved the highest explanatory power (R\u0026sup2; = 0.141). Knee Ex PT(BW) demonstrated greater maximal predictability with the TabPFN regressor (R\u0026sup2; = 0.208). Importantly, feature-importance patterns for both outcomes were directionally consistent with established biomechanical and rehabilitation principles. These two outcomes also represent clinically meaningful domains of early postoperative recovery, reflecting integrated functional mobility and objective neuromuscular capacity, respectively. Accordingly, subsequent multivariate structural analyses (CCA), model diagnostics, feature-importance evaluation, and OR comparisons focused on these two clinically and statistically stable prediction targets.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eStructural interdependence of functional recovery domains following TKA\u003c/h2\u003e \u003cp\u003eCCA was performed to examine relationships among key functional variables measured preoperatively and at 3 months postoperatively. In all panels, the horizontal axis represents preoperative values and the vertical axis represents the corresponding 3-month postoperative values, enabling visualization of associations between baseline status and postoperative recovery. This analysis aimed to identify patterns of interrelationships among major functional indicators during early recovery following TKA.\u003c/p\u003e \u003cp\u003eFirst, a statistically significant positive correlation was observed between preoperative SCT ascent performance and postoperative 6MWT performance at 3 months (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). This finding indicates that better baseline stair-ascent ability is associated with superior postoperative walking endurance, suggesting that shared physical components, such as lower limb strength, balance, and mobility, contribute to functional recovery.\u003c/p\u003e \u003cp\u003eSecond, a significant positive association was also identified between preoperative SCT descent performance and postoperative 6MWT performance at 3 months (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). Because stair descent requires coordinated control of knee extension and flexion, proprioceptive input, and balance, this correlation suggests that patients with better preoperative functional control tend to achieve more favorable postoperative endurance outcomes.\u003c/p\u003e \u003cp\u003eThird, preoperative Knee Ex PT(BW) showed a moderate positive correlation with postoperative Knee Ex PT(BW) at 3 months (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec). This relationship suggests that baseline knee extensor muscle strength is closely linked to postoperative recovery of knee extensor strength, reflecting a coordinated and interdependent recovery pattern within the lower-limb musculature following TKA.\u003c/p\u003e \u003cp\u003eFinally, a statistically significant association was observed between sex and postoperative Knee Ex PT(BW) at 3 months (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed), with higher extension strength values generally observed in male patients. This finding indicates that strength-related functional measures may vary by sex and should be interpreted accordingly.\u003c/p\u003e \u003cp\u003eTaken together, these CCA-based analyses demonstrate that baseline functional status and patient characteristics are strongly associated with postoperative walking ability, stair performance, and lower-limb muscle strength. These results highlight that functional recovery domains following TKA are interrelated rather than independent, particularly during the early postoperative period.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eDomain-dependent predictive performance of ML models in early postoperative recovery\u003c/h2\u003e \u003cp\u003eSeven ML regression models (Ada Boost Regressor, Extra Trees Regressor, Gradient Boosting Regressor, Random Forest Regressor, Historically Based Gradient Boosting Regressor, Support Vector Machine Regressor, and TabPFN Regressor) were trained to predict functional outcomes at 3 months postoperatively. Performance metrics, including MAE, MSE, RMSE, and R\u0026sup2;, are summarized in Supplementary Table\u0026nbsp;2.\u003c/p\u003e \u003cp\u003ePrediction of the 6MWT showed modest overall model performance, with R\u0026sup2; values remaining below 0.15 across all models. The Support Vector Machine Regressor demonstrated the highest explanatory power (R\u0026sup2; = 0.141) and the lowest RMSE (0.129), followed by the Random Forest and Extra Trees regressors, which showed comparable error magnitudes but lower R\u0026sup2; values. Overall, the 6MWT was more difficult to predict than other functional indicators, with R\u0026sup2; values falling below 0.10 in several models, indicating limited predictive capacity. These findings suggest that walking endurance is a complex outcome influenced by multiple physiological and behavioral factors, which constrain predictive accuracy.\u003c/p\u003e \u003cp\u003eIn contrast, the prediction of Knee Ex PT(BW) demonstrated comparatively greater explanatory power. The TabPFN Regressor achieved the best performance (R\u0026sup2; = 0.208, RMSE\u0026thinsp;=\u0026thinsp;0.123), followed closely by the Support Vector Machine Regressor (R\u0026sup2; = 0.196). Tree-based ensemble models exhibited variable performance, with some showing limited or negative R\u0026sup2; values. Overall, strength-based outcomes demonstrated greater predictability than walking-based outcomes. These findings indicate that optimal model performance depends on the prediction target and that complex functional measures, such as 6MWT, have relatively greater predictive difficulty due to the influence of multiple biological and environmental factors.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eResidual analysis and model stability assessment for postoperative outcome prediction\u003c/h2\u003e \u003cp\u003eResidual distribution analysis is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Residuals for the 6MWT were right-skewed and with several extreme values, suggesting heterogeneous individual walking recovery. In contrast, residuals for Knee Ex PT(BW) were more symmetrically distributed around zero, suggesting greater model stability for strength prediction.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents Q\u0026ndash;Q plots assessing residual normality for both outcomes. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea (6MWT), shows limited residual normality, with segments deviating from the theoretical quantile line, a pattern commonly observed when predicting walking ability from complex biomechanical factors. In contrast, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb (Knee Ex PT(BW)) indicates residuals that closely follow the reference line, indicating that strength-based indicators exhibit a more stable and consistent structure, and are therefore easier to model.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e illustrates prediction accuracy by directly comparing the actual value with the model-predicted value. In Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea, discrepancies appear between the measured and predicted values in some instances; however, both series follow a similar overall trend. This finding indicates that the model captures the general pattern of walking ability, although prediction errors may arise for cases with large individual variability. In Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb, the observed and predicted curves remain closely aligned in most instances, indicating greater prediction stability. Notably, the location and direction of peak changes show strong agreement, suggesting higher reliability of the model in predicting the strength index. Overall, these results indicate that the muscle strength index (Knee EX PT(BW)) demonstrates better model fit and greater predictive stability than the walking index (6MWT). The 6MWT is more difficult to predict because it reflects its multiple interacting factors, including physiological status, cardiopulmonary endurance, balance, and pain sensitivity, whereas Knee Ex PT(BW) is a more localized, structural indicator that allows for more precise model-based prediction.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eExplainable ML identifies structural determinants of strength and walking recovery\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e illustrates the importance of permutation-based features in models predicting functional outcomes (6MWT, Knee Ex PT(BW)) at 3 months post-surgery. Each subpanel quantifies the contribution of input variables to the predictive performance of the corresponding clinical indicators. In the 6MWT prediction model (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea), preoperative muscle strength-related variables\u0026mdash;specifically Knee Fl PT(BW) and Knee Ex PT(BW)\u0026mdash;had the highest importance scores (0.8682 and 0.2117, respectively). This finding is consistent with prior evidence indicating that gait endurance recovery is intrinsically dependent on lower-limb muscle strength. Additionally, preoperative SCT descent (0.1207) and SCT ascent (0.0795) demonstrated significant relative importance, reflecting shared biomechanical demands (balance, strength, and proprioception) between stair negotiation and walking ability.\u003c/p\u003e \u003cp\u003eOther variables, including preoperative TUG, 6MWT, EQ-5D, and VAS, contributed minimally, whereas demographic and medical factors (age, stroke, hypertension, etc.) had negligible impact on the prediction. Similarly, in the Knee Ex PT(BW) model (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb), preoperative knee muscle strength indicators were the dominant predictors. Knee Fl PT(BW) (1.0767) and Knee Ex PT(BW) (0.9804) ranked highest, demonstrating that postoperative knee extension strength recovery is closely linked to the preoperative status of the entire knee muscle group (agonist and antagonist). The high importance of knee flexor strength highlights the integrated biomechanical relationship required for functional movement. Preoperative SCT descent and ascent also emerged as important predictors, supporting the role of stair performance as a strong indicator of muscle function. In contrast, preoperative 6MWT, TUG, and EQ-5D showed only indirect effects, suggesting that local muscle strength recovery is driven more by strength-specific metrics than by general mobility measures. Overall, the feature importance analysis reveals a consistent pattern across both functional outcomes: preoperative knee muscle strength and stair performance are the primary determinants of functional recovery at 3 months, whereas demographic and medical history variables contribute little predictive value. These findings suggest that ML models for TKA recovery should prioritize functional and muscle-specific evaluations over static risk factors to significantly improve prediction accuracy.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWOMAC, The Western Ontario and McMaster Universities Osteoarthritis Index; VAS, visual analog scale; EQ-5D, EuroQol-5 dimension Questionnaires; TUG, timed up and go test; 6MWT, 6-minute walk test; SCT, stair climbing test; Knee Ex PT(BW), knee extension peak torque normalized to body weight; Knee Fl PT(BW), knee flexor peak torque normalized to body weight; TKA, total knee arthroplasty; BMI, body mass index; SMI, skeletal muscle index; CI, confidence interval.\u003c/p\u003e \u003cp\u003eFigure 6a\u0026ndash;b presents the LIME analysis results for the best-performing models using external validation data. The length of each bar represents the magnitude of a feature's contribution to the prediction of each functional outcome (6MWT, Knee Ex PT(BW)). In Fig.\u0026nbsp;6a (6MWT), key predictors included preoperative 6MWT, SCT (ascent/descent), and skeletal muscle index (SMI), indicating that gait endurance recovery is intrinsically linked to systemic muscle mass and stair negotiation ability\u0026mdash;a functional task requiring lower-limb strength, balance, and coordination. In Fig.\u0026nbsp;6b (Knee Ex PT(BW)), the most influential variables included preoperative Knee Ex PT(BW), SMI, Knee Fl PT(BW), and SCT (ascent/descent). This highlights that knee extensor strength recovery follows an integrated pattern structurally associated with knee flexor strength, muscle mass, and functional stair performance. Notably, SCT ascent/descent demonstrated strong explanatory power across both models, serving as a dual indicator of knee muscle strength and actual functional performance. Collectively, the LIME analysis results demonstrate that while walking ability and knee muscle strength share common functional predictors (e.g., SMI and SCT), they also retain distinct domain-specific determinants. The model's predictive logic largely aligns with established biomechanical relationships observed in clinical practice. These findings confirm that LIME-based interpretations provide valuable insights for identifying individual recovery patterns and establishing personalized rehabilitation strategies.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eComparative evaluation of traditional univariable risk estimation and ML prediction\u003c/h2\u003e \u003cp\u003e \u003cb\u003eFigure\u0026nbsp;6\u003c/b\u003e Model performance for predicting functional outcomes on external data assessed using LIME analysis. Each panel corresponds to a specific outcome: (a) 6MWT (3M), (b) Knee Ex PT (BW) (3M). WOMAC, The Western Ontario and McMaster Universities Osteoarthritis Index; VAS, visual analog scale; EQ-5D, EuroQol-5 dimension Questionnaires; TUG, timed up and go test; 6MWT, 6-minute walk test; SCT, stair climbing test; Knee Ex PT(BW), knee extension peak torque normalized to body weight; Knee Fl PT(BW), knee flexor peak torque normalized to body weight; TKA, total knee arthroplasty; BMI, body mass index; SMI, skeletal muscle index; CI, confidence interval; LIME, Local Interpretable Model-agnostic Explanations.\u003c/p\u003e \u003cp\u003eSupplementary Table\u0026nbsp;3 summarizes the ORs and 95% confidence intervals (CI) for variables associated with functional recovery (6MWT, Knee Ex PT(BW)) at 3 months post-surgery. No single variable reached the significance threshold (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.001), indicating that individual clinical and functional factors alone are insufficient to predict functional recovery. Nevertheless, the direction and magnitude of the ORs provide insights into potential trends underlying recovery.\u003c/p\u003e \u003cp\u003eFor 6MWT recovery, most variables showed ORs ranging from 0.5 to 1.5, indicating that no factor emerged as a dominant independent predictor. Advanced age (OR, 0.650) showed a tendency toward lower recovery potential, though this association was not statistically significant. Major clinical variables (surgical history and comorbidities) and functional indicators (TUG, 6MWT, SCT, and knee muscle strength) also lacked significant predictive power (OR, 0.394\u0026ndash;1.330). These findings underscore that walking endurance is a multifactorial outcome influenced by complex biomechanical and psychological interactions.\u003c/p\u003e \u003cp\u003eSimilarly, for Knee Ex PT(BW), most variables presented ORs between 0.7 and 1.5, reinforcing the difficulty of univariate prediction. Female sex (OR, 0.695) suggested a potential disadvantage in knee muscle strength restoration, while the SMI showed a relatively high association (OR, 2.129), reflecting a physiological link, although neither association reached statistical significance. This suggests that knee muscle strength recovery relies on a combination of factors\u0026mdash;such as neuromuscular activity, pain control, and rehabilitation adherence\u0026mdash;rather than simple baseline characteristics.\u003c/p\u003e \u003cp\u003eIn conclusion, these results demonstrate that functional recovery following TKA is a multidimensional process that cannot be adequately explained by single clinical variables. The limited explanatory power of univariate models highlights the complexity of the rehabilitation process. Therefore, these findings strongly support the necessity of adopting ML models capable of capturing high-dimensional interactions to improve prediction accuracy.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study developed and validated an explainable ML framework to predict early functional recovery following TKA using multidimensional preoperative data. At 3 months postoperatively, patients demonstrated significant improvements in pain, self-reported function, mobility, walking endurance, and knee muscle strength. Notably, the substantial gains in the 6MWT, along with improvements in stair performance and TUG, indicate meaningful restoration of functional mobility during the early postoperative period. Knee extensor and flexor strength also improved significantly, reflecting recovery of lower-limb muscle performance after surgery. Collectively, these findings confirm that multiple functional domains demonstrate measurable improvement within the first 3 months after TKA[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHowever, improvement in individual outcomes does not necessarily imply that these domains recover independently. Given the shared neuromuscular and biomechanical factors underlying postoperative function, muscle strength, mobility, and walking endurance may be structurally interrelated rather than isolated constructs [\u003cspan additionalcitationids=\"CR48\" citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. To further examine the structural relationships among these outcomes, CCA was performed. Postoperative functional outcomes did not behave independently, but rather clustered into interconnected recovery patterns. Walking endurance was closely associated with stair ascent and descent performance, while knee extensor strength demonstrated coordinated recovery [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. These findings indicate that early postoperative function after TKA is best understood as an integrated system rather than a collection of isolated measures. Consequently, impairments in a single domain, such as stair negotiation, may reflect broader limitations in overall mobility and lower-limb muscle performance.\u003c/p\u003e \u003cp\u003eBuilding on this integrated recovery pattern, we examined whether different functional domains exhibited comparable levels of predictive accuracy when modeled using multidimensional preoperative data. ML analyses revealed that predictive performance differed across outcomes. Among the evaluated measures, knee extensor strength demonstrated greater predictability than the 6MWT. This finding is consistent with the physiological characteristics of the two outcomes. Knee extensor strength reflects a localized, structurally constrained function, largely determined by neuromuscular capacity and muscle mass, whereas walking endurance is a composite ability influenced by cardiopulmonary fitness, balance, pain perception, motivation, and environmental factors. Therefore, the lower predictive performance observed for the 6MWT likely reflects the inherent complexity of gait-related recovery rather than limitations of the modeling approach itself. The modest R\u003csup\u003e2\u003c/sup\u003e values observed in this study are comparable to those reported in previous prognostic studies of postoperative function and underscore the challenges of predicting complex functional behaviors in heterogeneous older populations.\u003c/p\u003e \u003cp\u003eDifferences in predictive performance were further supported by residual and model-fit analyses. Predictions of knee extensor strength exhibited relatively symmetric residual distributions and strong agreement between predicted and observed values, indicating stable and consistent model performance. In contrast, residuals for 6MWT were more heterogeneous, with deviations from normality and occasional extreme values. Clinically, this variability likely reflects individual differences in pain tolerance, balance confidence, cardiopulmonary reserve, and activity engagement during early recovery, underscoring the multifactorial determinants of walking endurance.\u003c/p\u003e \u003cp\u003eBeyond overall predictive performance, examining influential predictors provides additional insight into the determinants of postoperative recovery. Feature importance analyses consistently identified preoperative knee muscle strength and stair-climbing performance as the most influential predictors of both walking endurance and knee extensor strength. Stair-climbing tasks integrate concentric and eccentric muscle control, balance, and coordination, making them sensitive indicators of lower-limb functional reserve. The repeated importance of SMI further emphasizes the role of systemic muscle mass in early postoperative recovery, particularly among older adults at risk of sarcopenia. Notably, knee flexor strength contributed substantially to the prediction of knee extensor strength recovery, highlighting the coordinated agonist-antagonist relationship required for stable knee function. This finding suggests that comprehensive knee muscle conditioning, rather than isolated quadriceps strengthening alone, may be critical for optimal recovery.\u003c/p\u003e \u003cp\u003eTo enhance individual-level interpretability, LIME was applied to generate patient-specific explanations of model predictions. The resulting explanations demonstrated clinically plausible patterns, illustrating how functional and strength-related factors influenced predicted outcomes for individual patients. The concordance between model-derived explanations and established biomechanical principles strengthens the clinical credibility of the proposed framework and may facilitate its integration into rehabilitation decision-making. However, not all postoperative outcomes demonstrated sufficient stability or explainability to support in-depth predictive modeling.\u003c/p\u003e \u003cp\u003eCareful selection of prediction targets was also essential to ensure model stability and explainability. Although nine postoperative functional outcomes showed statistically significant improvement, only 6MWT and knee extensor strength demonstrated reproducible predictive performance and consistent validation behavior across algorithms. Most patient-reported and general mobility measures exhibited limited explanatory power and unstable validation results, suggesting that these outcomes may be influenced by contextual and psychosocial factors not fully captured by structured preoperative variables. Focusing on walking endurance and knee extensor strength enabled more robust multivariable modeling and clearer evaluation of the incremental value of ML beyond traditional analytical approaches.\u003c/p\u003e \u003cp\u003eIn contrast to multivariable ML models, univariate OR analyses failed to identify statistically significant predictors of postoperative recovery. This finding highlights the limitations of isolated demographic or clinical variables in explaining functional variability after TKA and further supports the multidimensional nature of rehabilitation. Rather than representing a negative finding, the absence of strong univariate associations underscores the need for analytical approaches capable of modeling nonlinear interactions among multiple contributing factors.\u003c/p\u003e \u003cp\u003eClinically, these findings suggest that modifiable functional characteristics\u0026mdash;particularly knee muscle strength, stair negotiation ability, and muscle mass\u0026mdash;play a more central role in early postoperative recovery than static demographic characteristics or medical comorbidities. This supports the implementation of targeted prehabilitation and early postoperative rehabilitation strategies emphasizing strength enhancement and task-specific functional training. Therefore, explainable ML models may serve as complementary decision-support tools to identify patients who would benefit most from individualized or intensified rehabilitation programs.\u003c/p\u003e \u003cp\u003eSeveral limitations should be acknowledged. First, the study population included a higher proportion of female patients, consistent with the epidemiology of knee osteoarthritis and TKA in older populations. Although sex was identified as a contributing factor in some analyses, its overall predictive influence was modest, and the primary findings regarding functional recovery patterns remained robust. Second, this was a single-center retrospective study, which may limit generalizability. Third, predictive performance for walking endurance remained modest despite rigorous validation, reflecting the complexity of gait-related outcomes. Fourth, although nine postoperative outcomes were initially explored, only two demonstrated sufficient predictive stability for in-depth modeling, which may limit generalizability to other functional domains. Finally, only structured clinical data were analyzed. Future studies should incorporate longitudinal assessments, unstructured clinical data, and multicenter cohorts to further refine predictive accuracy and enhance clinical applicability.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study demonstrates that an explainable ML framework can predict early functional recovery following TKA using multidimensional preoperative data. Knee extensor strength showed greater predictability than walking endurance, reflecting the more localized and structurally constrained nature of strength recovery. In addition, preoperative knee muscle strength, stair-climbing ability, and skeletal muscle mass emerged as key determinants of early postoperative function, whereas demographic characteristics and medical comorbidities contributed minimally. Explainable artificial intelligence enabled transparent, patient-specific explanations of predictions, supporting the potential use of these models as adjunctive tools for personalized rehabilitation planning after TKA.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eTKA \u0026nbsp; \u0026nbsp; total knee arthroplasty\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003eNone\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eB.R.K. conceptualized and designed the study.\u003cbr\u003e\u0026nbsp;B.R.K., J.H.C. collected and curated the clinical data.\u003cbr\u003e\u0026nbsp;S.H.C. developed the machine learning models and performed the statistical analysis.\u003cbr\u003e\u0026nbsp;B.R.K., Y.M.K and J.T.L. interpreted the data and validated the results.\u003cbr\u003e\u0026nbsp;J.T.L. and M.S.K. drafted the manuscript.\u003cbr\u003e\u0026nbsp;All authors critically revised the manuscript and approved the final version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (RS-2024-00336696, RS-2026-25469859).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability:\u0026nbsp;\u003c/strong\u003eThe original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e: The authors declare no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e This retrospective cohort study was approved by the Institutional Review Board (IRB) of Korea University Anam Hospital (IRB No: 2022AN0110). The requirement for informed consent was waived by the IRB due to the retrospective design and use of de-identified data. All procedures were conducted in accordance with the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003ePrice AJ, Alvand A, Troelsen A, Katz JN, Hooper G, Gray A, et al. Knee replacement. Lancet. 2018;392:1672\u0026ndash;82. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/s0140-6736(18)32344-4\u003c/span\u003e\u003cspan address=\"10.1016/s0140-6736(18)32344-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDuong V, Oo WM, Ding C, Culvenor AG, Hunter DJ. Evaluation and Treatment of Knee Pain: A Review. JAMA. 2023;330:1568\u0026ndash;80. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1001/jama.2023.19675\u003c/span\u003e\u003cspan address=\"10.1001/jama.2023.19675\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKatz JN, Arant KR, Loeser RF. Diagnosis and Treatment of Hip and Knee Osteoarthritis: A Review. JAMA. 2021;325:568\u0026ndash;78. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1001/jama.2020.22171\u003c/span\u003e\u003cspan address=\"10.1001/jama.2020.22171\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOrange GM, Hince DA, Travers MJ, Stanton TR, Jones M, Sharma S, et al. Physical Function Following Total Knee Arthroplasty for Osteoarthritis: A Longitudinal Systematic Review With Meta-analysis. J Orthop Sports Phys Ther. 2025;55:1\u0026ndash;11. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2519/jospt.2024.12570\u003c/span\u003e\u003cspan address=\"10.2519/jospt.2024.12570\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHylkema TH, Brouwer S, Stewart RE, van Beveren J, Rijk PC, Brouwer RW, et al. Two-year recovery courses of physical and mental impairments, activity limitations, and participation restrictions after total knee arthroplasty among working-age patients. Disabil Rehabil. 2022;44:291\u0026ndash;300. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/09638288.2020.1766583\u003c/span\u003e\u003cspan address=\"10.1080/09638288.2020.1766583\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWylde V, Penfold C, Rose A, Blom AW. Variability in long-term pain and function trajectories after total knee replacement: A cohort study. Orthop Traumatol Surg Res. 2019;105:1345\u0026ndash;50. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.otsr.2019.08.014\u003c/span\u003e\u003cspan address=\"10.1016/j.otsr.2019.08.014\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eConnelly JW, Galea VP, Rojanasopondist P, Nielsen CS, Bragdon CR, Kappel A, et al. Which Preoperative Factors are Associated with Not Attaining Acceptable Levels of Pain and Function After TKA? Findings from an International Multicenter Study. Clin Orthop Relat Res. 2020;478:1019\u0026ndash;28. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1097/corr.0000000000001162\u003c/span\u003e\u003cspan address=\"10.1097/corr.0000000000001162\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNanjo K, Ikeda T, Nagashio N, Sakai T, Jinno T. Preoperative Predictors of Instrumental Activities of Daily Living Disability in Older Adults 6 Months After Knee Arthroplasty. Arch Phys Med Rehabil. 2023;104:1260\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.apmr.2023.02.007\u003c/span\u003e\u003cspan address=\"10.1016/j.apmr.2023.02.007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHewlett-Smith N, Pope R, Furness J, Simas V, Hing W. Prognostic factors for inpatient functional recovery following total hip and knee arthroplasty: a systematic review. Acta Orthop. 2020;91:313\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/17453674.2020.1744852\u003c/span\u003e\u003cspan address=\"10.1080/17453674.2020.1744852\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKatzman JL, Cardillo C, Schaffler BC, Schwarzkopf R, Rozell JC. Can Preoperative Patient-Reported Outcome Measures Predict Clinical Outcomes Following Total Knee Arthroplasty? J Arthroplasty. 2026;41:1064\u0026ndash;74. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.arth.2025.08.075\u003c/span\u003e\u003cspan address=\"10.1016/j.arth.2025.08.075\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eElfanagely O, Toyoda Y, Othman S, Mellia JA, Basta M, Liu T, et al. Machine Learning and Surgical Outcomes Prediction: A Systematic Review. J Surg Res. 2021;264:346\u0026ndash;61. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jss.2021.02.045\u003c/span\u003e\u003cspan address=\"10.1016/j.jss.2021.02.045\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOgink PT, Groot OQ, Karhade AV, Bongers MER, Oner FC, Verlaan JJ, et al. Wide range of applications for machine-learning prediction models in orthopedic surgical outcome: a systematic review. Acta Orthop. 2021;92:526\u0026ndash;31. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/17453674.2021.1932928\u003c/span\u003e\u003cspan address=\"10.1080/17453674.2021.1932928\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKarimi AH, Langberg J, Malige A, Rahman O, Abboud JA, Stone MA. Accuracy of machine learning to predict the outcomes of shoulder arthroplasty: a systematic review. Arthroplasty. 2024;6:26. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s42836-024-00244-4\u003c/span\u003e\u003cspan address=\"10.1186/s42836-024-00244-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAydin M, Orhan F. Evaluating the Impact of AI in Orthopedics: A Quantitative Analysis of Advancements and Challenges. Bratislava Med J. 2025;126:1858\u0026ndash;75. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s44411-025-00170-0\u003c/span\u003e\u003cspan address=\"10.1007/s44411-025-00170-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCiobanu-Caraus O, Aicher A, Kernbach JM, Regli L, Serra C, Staartjes VE. A critical moment in machine learning in medicine: on reproducible and interpretable learning. Acta Neurochir (Wien). 2024;166:14. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00701-024-05892-8\u003c/span\u003e\u003cspan address=\"10.1007/s00701-024-05892-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAdadi A, Berrada M. Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI). IEEE Access. 2018;6:52138\u0026ndash;60. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1109/access.2018.2870052\u003c/span\u003e\u003cspan address=\"10.1109/access.2018.2870052\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRudin C. Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead. Nat Mach Intell. 2019;1:206\u0026ndash;15. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s42256-019-0048-x\u003c/span\u003e\u003cspan address=\"10.1038/s42256-019-0048-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRibeiro MT, Singh S, Guestrin C. Association for Computing Machinery. Why Should I Trust You? Explaining the Predictions of Any Classifier. 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD). San Francisco, CA2016. pp. 1135\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMienye ID, Obaido G, Jere N, Mienye E, Aruleba K, Emmanuel ID, et al. A survey of explainable artificial intelligence in healthcare: Concepts, applications, and challenges. Inf Med Unlocked. 2024;51:101587. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/https://doi.org/10.1016/j.imu.2024.101587\u003c/span\u003e\u003cspan address=\"10.1016/j.imu.2024.101587\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTopol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25:44\u0026ndash;56. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41591-018-0300-7\u003c/span\u003e\u003cspan address=\"10.1038/s41591-018-0300-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang B, Liu SM, Zhao YS, IEEE. Research on Modeling and Application of Magneto-Optical Measurement Technology. 3rd IEEE Student Conference on Electrical Machines and Systems (SCEMS). Jinan, PEOPLES R CHINA2020. pp. 207\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBellamy N, Buchanan WW, Goldsmith CH, Campbell J, Stitt LW. Validation study of WOMAC: a health status instrument for measuring clinically important patient relevant outcomes to antirheumatic drug therapy in patients with osteoarthritis of the hip or knee. J Rheumatol. 1988;15:1833\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWhite DK, Master H. Patient-Reported Measures of Physical Function in Knee Osteoarthritis. Rheum Dis Clin North Am. 2016;42:239\u0026ndash;52. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.rdc.2016.01.005\u003c/span\u003e\u003cspan address=\"10.1016/j.rdc.2016.01.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWoolacott NF, Corbett MS, Rice SJ. The use and reporting of WOMAC in the assessment of the benefit of physical therapies for the pain of osteoarthritis of the knee: findings from a systematic review of clinical trials. Rheumatology (Oxford). 2012;51:1440\u0026ndash;6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/rheumatology/kes043\u003c/span\u003e\u003cspan address=\"10.1093/rheumatology/kes043\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAngst F, Ewert T, Lehmann S, Aeschlimann A, Stucki G. The factor subdimensions of the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) help to specify hip and knee osteoarthritis. a prospective evaluation and validation study. J Rheumatol. 2005;32:1324\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEbrahimzadeh MH, Makhmalbaf H, Birjandinejad A, Keshtan FG, Hoseini HA, Mazloumi SM. The Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) in Persian Speaking Patients with Knee Osteoarthritis. Arch Bone Jt Surg. 2014;2:57\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcConnell S, Kolopack P, Davis AM. The Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC): a review of its utility and measurement properties. Arthritis Rheum. 2001;45:453\u0026ndash;61. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/1529-0131(200110)45:5%3C453::aid-art365%3E3.0.co;2-w\u003c/span\u003e\u003cspan address=\"10.1002/1529-0131(200110)45:5%3C453::aid-art365%3E3.0.co;2-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuskisson EC. Measurement of pain. Lancet. 1974;2:1127\u0026ndash;31. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/s0140-6736(74)90884-8\u003c/span\u003e\u003cspan address=\"10.1016/s0140-6736(74)90884-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBijur PE, Silver W, Gallagher EJ. Reliability of the visual analog scale for measurement of acute pain. Acad Emerg Med. 2001;8:1153\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1553-2712.2001.tb01132.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1553-2712.2001.tb01132.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEuroQol Group. EuroQol\u0026ndash;a new facility for the measurement of health-related quality of life. Health Policy (New York). 1990;16:199\u0026ndash;208. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/0168-8510(90)90421-9\u003c/span\u003e\u003cspan address=\"10.1016/0168-8510(90)90421-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRabin R, de Charro F. EQ-5D: a measure of health status from the EuroQol Group. Ann Med. 2001;33:337\u0026ndash;43. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3109/07853890109002087\u003c/span\u003e\u003cspan address=\"10.3109/07853890109002087\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eConner-Spady BL, Marshall DA, Bohm E, Dunbar MJ, Noseworthy TW. Comparing the validity and responsiveness of the EQ-5D-5L to the Oxford hip and knee scores and SF-12 in osteoarthritis patients 1 year following total joint replacement. Qual Life Res. 2018;27:1311\u0026ndash;22. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11136-018-1808-5\u003c/span\u003e\u003cspan address=\"10.1007/s11136-018-1808-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBilbao A, Garc\u0026iacute;a-P\u0026eacute;rez L, Arenaza JC, Garc\u0026iacute;a I, Ariza-Cardiel G, Trujillo-Mart\u0026iacute;n E, et al. Psychometric properties of the EQ-5D-5L in patients with hip or knee osteoarthritis: reliability, validity and responsiveness. Qual Life Res. 2018;27:2897\u0026ndash;908. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11136-018-1929-x\u003c/span\u003e\u003cspan address=\"10.1007/s11136-018-1929-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePodsiadlo D, Richardson S. The timed Up \u0026amp; Go: a test of basic functional mobility for frail elderly persons. J Am Geriatr Soc. 1991;39:142\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1532-5415.1991.tb01616.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1532-5415.1991.tb01616.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOnodera CMK, Coelho-J\u0026uacute;nior HJ, Sampaio RAC, Santos Duarte Lana JF, Teixeira LFM, Uchida MC, et al. The importance of objectively measuring functional tests in complement to self-report assessments in patients with knee osteoarthritis. Gait Posture. 2020;82:33\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.gaitpost.2020.08.121\u003c/span\u003e\u003cspan address=\"10.1016/j.gaitpost.2020.08.121\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNightingale CJ, Mitchell SN, Butterfield SA. Validation of the Timed Up and Go Test for Assessing Balance Variables in Adults Aged 65 and Older. J Aging Phys Act. 2019;27:230\u0026ndash;3. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1123/japa.2018-0049\u003c/span\u003e\u003cspan address=\"10.1123/japa.2018-0049\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZasadzka E, Borowicz AM, Roszak M, Pawlaczyk M. Assessment of the risk of falling with the use of timed up and go test in the elderly with lower extremity osteoarthritis. Clin Interv Aging. 2015;10:1289\u0026ndash;98. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2147/cia.S86001\u003c/span\u003e\u003cspan address=\"10.2147/cia.S86001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eATS Committee on Proficiency Standards for Clinical Pulmonary Function Laboratories. ATS statement: guidelines for the six-minute walk test. Am J Respir Crit Care Med. 2002;166:111\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1164/ajrccm.166.1.at1102\u003c/span\u003e\u003cspan address=\"10.1164/ajrccm.166.1.at1102\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGacto-S\u0026aacute;nchez M, Lozano-Meca JA, Lozano-Guadalajara JV, Montilla-Herrador J. Concurrent validity of the 2-and 6-minute walk test in knee osteoarthritis. Knee. 2023;43:34\u0026ndash;41. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.knee.2023.05.009\u003c/span\u003e\u003cspan address=\"10.1016/j.knee.2023.05.009\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOzsoy G, Kodak MI, Kararti C, Ozyurt F, Kodak SB, Karacay BC, et al. Optimal cut-off scores of performance-based tests of physical function to discriminate disease severity in patients with knee osteoarthritis. Knee. 2025;55:179\u0026ndash;85. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.knee.2025.04.019\u003c/span\u003e\u003cspan address=\"10.1016/j.knee.2025.04.019\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRamalho RB, Casonato NA, Montilha VB, Chaves TC, Mattiello SM, Selistre LFA. Construct Validity and Responsiveness of Performance-based Tests in Individuals With Knee Osteoarthritis. Arch Phys Med Rehabil. 2024;105:1862\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.apmr.2024.05.024\u003c/span\u003e\u003cspan address=\"10.1016/j.apmr.2024.05.024\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSharma S, Wilson R, Pryymachenko Y, Pathak A, Chua J, Gwynne-Jones D, et al. Reliability, Validity, Responsiveness, and Minimum Important Change of the Stair Climb Test in Adults With Hip and Knee Osteoarthritis. Arthritis Care Res (Hoboken). 2023;75:1147\u0026ndash;57. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/acr.24821\u003c/span\u003e\u003cspan address=\"10.1002/acr.24821\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eS\u0026oslash;rensen B, Aagaard P, Coupp\u0026eacute; C, Suetta C, Johannsen FE, Magnusson SP. Stair climb muscle power is associated with gait speed, sit-to-stand performance, patient-reported outcomes and objective measures of mechanical muscle function in individuals with knee osteoarthritis - secondary analysis from an RCT. Musculoskelet Sci Pract. 2025;77:103332. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.msksp.2025.103332\u003c/span\u003e\u003cspan address=\"10.1016/j.msksp.2025.103332\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCasa\u0026ntilde;a J, Calatayud J, Silvestre A, S\u0026aacute;nchez-Frutos J, Andersen LL, Jakobsen MD, et al. Knee Extensor Muscle Strength Is More Important Than Postural Balance for Stair-Climbing Ability in Elderly Patients with Severe Knee Osteoarthritis. Int J Environ Res Public Health. 2021;18:3637. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/ijerph18073637\u003c/span\u003e\u003cspan address=\"10.3390/ijerph18073637\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHammami N, Bouzouraa E, \u0026Ouml;lmez C, Hattabi S, Mhimdi N, Khezami MA, et al. Isokinetic Knee Strengthening Impact on Physical and Functional Performance, Pain Tolerance, and Quality of Life in Overweight/Obese Women with Patellofemoral Pain Syndrome. J Clin Med. 2024;13:4696. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/jcm13164696\u003c/span\u003e\u003cspan address=\"10.3390/jcm13164696\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBerghmans DDP, Lenssen AF, Emans PJ, de Bie RA. Functions, disabilities and perceived health in the first year after total knee arthroplasty; a prospective cohort study. BMC Musculoskelet Disord. 2018;19:250. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12891-018-2159-7\u003c/span\u003e\u003cspan address=\"10.1186/s12891-018-2159-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChristensen JC, Capin JJ, Hinrichs LA, Aljehani M, Stevens-Lapsley JE, Zeni JA. Gait mechanics are influenced by quadriceps strength, age, and sex after total knee arthroplasty. J Orthop Res. 2021;39:1523\u0026ndash;32. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/jor.24878\u003c/span\u003e\u003cspan address=\"10.1002/jor.24878\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChoi JH, Kim BR, Kim SR, Nam KW, Lee SY, Suh MJ. Performance-based physical function correlates with walking speed and distance at 3 months post unilateral total knee arthroplasty. Gait Posture. 2021;87:163\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.gaitpost.2021.04.041\u003c/span\u003e\u003cspan address=\"10.1016/j.gaitpost.2021.04.041\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSuh MJ, Kim BR, Kim SR, Han EY, Nam KW, Lee SY, et al. Bilateral Quadriceps Muscle Strength and Pain Correlate With Gait Speed and Gait Endurance Early After Unilateral Total Knee Arthroplasty: A Cross-sectional Study. Am J Phys Med Rehabil. 2019;98:897\u0026ndash;905. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1097/phm.0000000000001222\u003c/span\u003e\u003cspan address=\"10.1097/phm.0000000000001222\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-orthopaedic-surgery-and-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"josr","sideBox":"Learn more about [Journal of Orthopaedic Surgery and Research](http://josr-online.biomedcentral.com)","snPcode":"13018","submissionUrl":"https://submission.nature.com/new-submission/13018/3","title":"Journal of Orthopaedic Surgery and Research","twitterHandle":"@MSKmedBMC","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Arthroplasty, Replacement, Knee, Recovery of Function, Artificial Intelligence, Models, Statistical, Muscle Strength","lastPublishedDoi":"10.21203/rs.3.rs-9408042/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9408042/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eTo develop and validate an explainable machine learning framework for predicting early functional recovery at 3 months following total knee arthroplasty (TKA) and to identify key patient-specific predictors using artificial intelligence (AI).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis retrospective cohort study included 313 patients who underwent primary TKA for end-stage knee osteoarthritis. Nine postoperative functional and patient-reported outcomes were assessed at baseline and at 3 months after surgery, including pain, mobility, quality of life, walking endurance, and knee muscle strength. Pre\u0026ndash;post comparisons were performed to characterize overall recovery patterns. To identify appropriate prediction targets, exploratory screening was performed using seven machine learning algorithms with nested cross-validation. Canonical correlation analysis was applied to examine structural relationships among selected outcomes. Model diagnostics, feature importance analyses, and odds ratio comparisons were subsequently performed. Machine learning model performance was evaluated using mean absolute error, root mean squared error, and coefficient of determination (R\u0026sup2;). Local Interpretable Model-agnostic Explanations (LIME) were applied to enhance model interpretability.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe mean age of the 313 patients was 71.8\u0026thinsp;\u0026plusmn;\u0026thinsp;5.9 years, and 49 patients (15.7%) were male. All nine outcomes demonstrated significant postoperative improvement (all p\u0026thinsp;\u0026le;\u0026thinsp;0.001), indicating global early recovery. Exploratory modeling identified the 6-minute walk test (6MWT) and knee extension peak torque normalized to body weight (Knee Ex PT(BW)) as the only outcomes demonstrating stable predictive performance. Although both improved significantly postoperatively, prediction of Knee Ex PT(BW) showed greater explanatory power (best R\u0026sup2; = 0.208) than the 6MWT (best R\u0026sup2; = 0.141). Baseline muscle strength, stair-climbing performance, and skeletal muscle index were consistently identified as key predictors across models, whereas demographic and comorbidity variables showed minimal contribution. LIME-based explanations revealed patient-specific patterns concordant with established biomechanical and functional relationships.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eExplainable machine learning models can identify meaningful patterns of early functional recovery after TKA. The 6MWT and Knee Ex PT(BW) were informative indicators of postoperative function and prognosis, with strength-based outcomes demonstrating particularly clear and interpretable predictive patterns. Explainable AI enabled transparent identification of patient-specific factors, providing a novel framework to support individualized rehabilitation planning.\u003c/p\u003e","manuscriptTitle":"Development and validation of machine learning models for predicting functional outcomes following total knee arthroplasty","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-12 16:54:38","doi":"10.21203/rs.3.rs-9408042/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-11T07:43:26+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-10T19:10:08+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-10T12:59:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"10139404638996959114643262797323198759","date":"2026-05-06T09:55:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"93564315462374800177768375398989088130","date":"2026-05-05T12:54:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"240138458056970861478545291339193249911","date":"2026-05-05T12:38:33+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-05T11:31:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"42902668536159258721096808050164486285","date":"2026-05-05T10:13:31+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-04T19:28:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"66158162362572535133581920734729232786","date":"2026-05-04T17:47:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"296687155732931552298392493303429987559","date":"2026-05-04T15:02:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"113863079247377867391996810672933641497","date":"2026-05-04T13:51:46+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-05-04T12:14:43+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-16T04:31:45+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-16T04:31:32+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Orthopaedic Surgery and Research","date":"2026-04-13T19:55:29+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-orthopaedic-surgery-and-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"josr","sideBox":"Learn more about [Journal of Orthopaedic Surgery and Research](http://josr-online.biomedcentral.com)","snPcode":"13018","submissionUrl":"https://submission.nature.com/new-submission/13018/3","title":"Journal of Orthopaedic Surgery and Research","twitterHandle":"@MSKmedBMC","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8a3f7e92-8d53-4b19-927a-e72ee4d914b8","owner":[],"postedDate":"May 12th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-11T07:43:26+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-10T19:10:08+00:00","index":46,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-10T12:59:52+00:00","index":45,"fulltext":""},{"type":"reviewerAgreed","content":"10139404638996959114643262797323198759","date":"2026-05-06T09:55:07+00:00","index":44,"fulltext":""},{"type":"reviewerAgreed","content":"93564315462374800177768375398989088130","date":"2026-05-05T12:54:45+00:00","index":42,"fulltext":""},{"type":"reviewerAgreed","content":"240138458056970861478545291339193249911","date":"2026-05-05T12:38:33+00:00","index":41,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-05T11:31:55+00:00","index":40,"fulltext":""},{"type":"reviewerAgreed","content":"42902668536159258721096808050164486285","date":"2026-05-05T10:13:31+00:00","index":39,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-04T19:28:32+00:00","index":37,"fulltext":""},{"type":"reviewerAgreed","content":"66158162362572535133581920734729232786","date":"2026-05-04T17:47:15+00:00","index":36,"fulltext":""},{"type":"reviewerAgreed","content":"296687155732931552298392493303429987559","date":"2026-05-04T15:02:46+00:00","index":34,"fulltext":""},{"type":"reviewerAgreed","content":"113863079247377867391996810672933641497","date":"2026-05-04T13:51:46+00:00","index":31,"fulltext":""},{"type":"reviewersInvited","content":"20","date":"2026-05-04T12:14:43+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-17T01:23:07+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-12 16:54:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9408042","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9408042","identity":"rs-9408042","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}