A novel web-based online nomogram was used to predict postoperative function in patients with femoral shaft fractures treated with intramedullary nails

A novel web-based online nomogram was used to predict postoperative function in patients with femoral shaft fractures treated with intramedullary nails | 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 A novel web-based online nomogram was used to predict postoperative function in patients with femoral shaft fractures treated with intramedullary nails Tao Zhang, Guangzhao Hou, Qian Xiao, Zhiyong Hou, Yingze Zhang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7080815/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Objective: To investigate factors influencing postoperative functional recovery after intramedullary nailing (IMN) for femoral shaft fractures and to develop a clinical prediction model. Methods: Patients who underwent IMN for femoral shaft fractures at the Third Hospital of Hebei Medical University between January 1, 2019, and December 31, 2023, were enrolled. Data were retrospectively collected. Patients treated in 2021 formed the validation cohort; patients treated in other years formed the modeling cohort. Least absolute shrinkage and selection operator (LASSO) regression identified factors influencing postoperative functional outcomes. Multivariable logistic regression analysis identified independent predictors, and a predictive model was constructed. Developed an interactive web-based calculator via R Shiny to implement the predictive model for postoperative recovery after intramedullary nailing of femoral shaft fractures. Results: This study included 672 patients who underwent IMN for femoral shaft fractures. The modeling cohort comprised 545 patients and the validation cohort 127 patients. Overall, 546 patients (81.2%) achieved good functional scores and exhibited satisfactory fracture healing. The mean age was 50.22 ± 17.73 years; males predominated (455 [67.70%]) over females (217 [32.30%]), yielding a male-to-female ratio of 2.10:1. LASSO regression identified significant predictors of postoperative recovery. Multivariable analysis revealed age 29-58 years, BMI 18.5-27.9 kg/m², and early appropriate weight-bearing as protective factors for good recovery. AO/OTA type C fracture, diabetes mellitus (DM), osteoporosis, and open reduction were independent risk factors for adverse outcomes. The model demonstrated strong discriminatory power, with C-indices of 0.895 (95% CI: 0.8654–0.9245) for the training cohort and 0.7848 (95% CI: 0.6947–0.8749) for the validation cohort. Hosmer-Lemeshow (H-L) testing indicated good model calibration. Decision curve analysis (DCA) showed optimal clinical utility when the threshold probability ranged from 0.19 to 1.00. The interactive web calculator developed via R Shiny is accessible at: https://femoralshaftfracture.shinyapps.io/DynNomapp1/. Conclusion: The developed predictive tool provides realistic, personalized postoperative outcome expectations for patients undergoing IMN for femoral shaft fractures. It can also aid clinicians in formulating appropriate surgical plans, ultimately improving patient prognosis. Femoral shaft fractures LASSO Clinical prediction model Web-based online nomogram Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction Femoral shaft fractures are among the relatively common high-energy trauma-induced long bone fractures in adults, accounting for 3–6% of all fractures[ 1 – 3 ]. In Western Europe and North America, the annual incidence approximates 10–21 per 100,000 people[ 4 – 5 ], while domestic data in China report an incidence of 19 per 100,000 people, though these rely solely on statistics for pediatric femoral shaft fractures[ 6 ]. Closed femoral shaft fractures, characterized by relatively mild soft tissue injury and lower risk of infection, are considered to have superior prognosis compared to open fractures, but still retain challenges for postoperative healing[ 7 ]. Since Kuntscher introduced the intramedullary nailing (IMN) technique in the 1940s, closed reduction combined with interlocking intramedullary nailing has become the internationally recognized preferred treatment[ 8 – 10 ]. IMN fixation, via a minimally invasive approach, preserves the peri-fracture soft tissue blood supply while providing biomechanical stability aligned with the femoral axis, enabling early weight-bearing. Furthermore, it demonstrates significantly lower complication rates compared to plate fixation[ 11 – 13 ]. Currently, IMN constitutes over 80% of surgical interventions for femoral shaft fractures in European and North American trauma centers[ 14 ], and has emerged as the primary surgical approach for such fractures in most Class A tertiary hospitals in China[ 15 ]. However, clinical practice reveals substantial variations in outcomes among patients receiving standardized IMN procedures, with a relatively high incidence of delayed union or nonunion[ 16 – 19 ]. Postoperative prognosis is collectively influenced by multiple factors, including patient-specific conditions, fracture classification, and surgical techniques[ 20 – 22 ]. At present, orthopedic surgeons primarily rely on clinical experience for risk assessment, lacking quantifiable and generalizable predictive tools[ 23 ]. Nevertheless, recent predictive models for femoral fracture healing or complications exhibit significant limitations: inadequate sample sizes and insufficient predictor diversity result in limited model robustness[ 24 – 27 ]. Consequently, developing a multidimensional prediction tool, internally and externally validated, holds substantial clinical value for postoperative risk stratification in closed femoral shaft fractures. This approach may reduce suboptimal recovery rates, provide evidence-based decision support for clinicians, improve patient prognosis, and enhance quality of life. 2. Materials and Methods 2.1 Data Collection This retrospective study complies with the principles of the Declaration of Helsinki. The study population consisted of patients who underwent treatment for femoral shaft fractures at the Third Hospital of Hebei Medical University between January 1, 2019, and December 31, 2023. All basic clinical data were obtained from the hospital's medical records, and all patients provided informed consent. The study design was approved by the Ethics Committee of the Hebei Medical University Third Hospital (Approval Number: K2015-002-1). Clinical trial number: not applicable. 2.2 Inclusion and Exclusion Criteria Inclusion criteria: 1) Unilateral closed femoral shaft fractures; 2) IMN via open or closed reduction techniques; 3) Age > 18 years; 4) Complete medical records and imaging data availability. Exclusion criteria: 1) Multiple ipsilateral lower limb fractures; 2) Fractures complicated by neurovascular injuries; 3) Pathological fractures, congenital deformities, or other diseases impairing lower limb function; 4) Non-acute fractures (> 3 weeks post-injury); 5) Severe systemic diseases (cardiac/cerebral/pulmonary) contraindicated for surgical intervention; 6) Psychiatric disorders impeding treatment compliance. 2.3 Predictor Variables Data were collected via telephone follow-up and medical record retrieval: (1) preoperative factors: gender, age, marriage, nationality, occupation, body mass index (BMI), season, AO/OTA classification, injury cause, preoperative basic diseases (hypocalcemia, diabetes, hypertension, coronary heart disease, osteoporosis, liver and biliary system diseases, etc.). (2) intraoperative factors: time-to-surgery, surgical mode (Open vs. closed reduction). (3) postoperative factors: time-to-weight-bearing initiation, hospital length of stay. Patients were divided into eight age groups: 19–28, 29–38, 39–48, 49–58, 59–68, 69–78, 79–88, and ≥ 89 years. According to the AO/OTA classification, fractures were classified as type A, B and C, which corresponded to simple, wedge and comminuted fractures, respectively. Early weight-bearing: Mobilization initiated on postoperative day 3 under physiotherapist guidance. Began with partial weight-bearing using assistive devices (e.g., crutches). Progressed to full weight-bearing as tolerated. 2.4 Study Outcomes Postoperative recovery was evaluated using the Lower Extremity Functional Scale (LEFS) developed by Binkley et al., combined with radiographic assessment. LEFS demonstrates high clinical feasibility for quantitative functional assessment. LEFS score > 70 points defined good functional recovery. Delayed union: Radiographic evidence of detectable but insufficient callus formation with persistent fracture line visibility at 4–8 months postoperatively, without sclerotic changes. Nonunion: Failure of healing with sclerotic fracture ends and bone resorption at 8–12 months postoperatively. Cases meeting criteria for delayed union or nonunion were classified as failure of fracture healing. 2.5 Statistical Analysis Statistical analyses were performed using R software version 4.3.0. All variables were converted into categorical variables, and frequency and proportion were used to describe the data. Chi-square test was used for inter-group comparisons; when the theoretical frequency of any cell in a 2×2 contingency table is less than 1 or the theoretical frequency of more than 20% cells in an R×C contingency table is less than 5, Fisher's exact test was applied. Patients from 2021 were randomly assigned to the validation cohort, while patients from other years were assigned to the training cohort. LASSO regression was used to reduce the dimensionality of the data, avoiding overfitting and ensuring model accuracy, to identify factors affecting postoperative functional outcomes. A 10-fold cross-validation was performed, and the optimal value was determined by the 1-standard error λ. Based on the characteristics of LASSO regression, multiple logistic regression was used to further analyze the variables and identify independent risk factors for poor recovery after femoral shaft fractures. In the logistic regression analysis table, beta (β) represents the regression coefficient, indicating the impact of the independent variable on the dependent variable in the regression equation. The standard error (SE) measures sampling error; the smaller the SE, the higher the reliability of inferring population parameters from sample statistics. The odds ratio (OR) reflects the correlation between the disease and exposure. A positive value indicates a positive correlation, while a negative value indicates a negative correlation, with the magnitude reflecting the strength of the correlation. The confidence interval (CI) is the interval of the estimated population parameter constructed using the sample statistic. The CI in the table is the confidence interval of the OR value. The final predictors identified through multivariable analysis were incorporated into a nomogram model predicting suboptimal recovery after closed femoral shaft fracture surgery. In the training cohort, we conducted internal validation using the bootstrap resampling process (n = 1000) and plotted calibration curves to assess the model's predictive accuracy. The ROC curve was used to evaluate the model's efficiency; a higher AUC indicates better predictive power. The DCA curve was also plotted to assess clinical effectiveness and to obtain net clinical benefit. The R Shiny package, an open-source toolkit for developing data-driven visualizations, was employed to construct an interactive web application framework that visually presents functional recovery outcomes following intramedullary nailing for femoral shaft fractures, with integrated automated risk calculation capabilities. All statistical tests were performed with two-tailed tests, and a p-value < 0.05 was considered significant. 3. Results 3.1 Cohort Characteristics Figure 1 illustrates participant inclusion/exclusion and group allocation. This study enrolled 794 eligible participants between January 2019 and December 2013; among them, 12 patients had ipsilateral lower extremity multiple fractures, 1 patient presented with a pathological fracture, and 4 patients had non-acute fractures. After 105 cases were lost to follow-up during the postoperative one-year period, 672 patients were included in the cohort for analysis following exclusions. Patients treated in 2021 were randomly assigned to the validation cohort, while those treated in other years formed the training cohort. Baseline characteristics between cohorts were comparable (P>0.05). Based on Schatzker-Lambert scoring and radiographic evaluation, 546 patients (81.2%) achieved good scores and showed satisfactory healing, whereas 126 patients (18.8%) had suboptimal scores or nonunion. The mean age was 50.22 ± 17.73 years, with 455 males (67.70%) and 217 females (32.30%), yielding a male-to-female ratio of 2.10:1. The 39-48 age group had the highest representation (128 cases, 19.04%). Regarding occupation, farmers constituted 309 cases (46.00%), BMI 18.5-23.9 kg/m² comprised 382 cases (56.85%), blood type B accounted for 229 cases (34.08%), hospitalization >14 days included 383 cases (57.00%), and summer admissions totaled 189 cases (28.12%). Detailed data are presented in Table 1. 3.2 Variable selection To prevent overfitting, LASSO regression was employed for variable selection (Figure 2). This was executed using the glmnet package in R, combined with a 10-fold cross-validation strategy to determine the optimal regularization parameter (λ). For a more parsimonious model, the λ value corresponding to one standard error was selected (Table 2). After screening, the collected variables were reduced to eight predictors: gender, age, early weight-bearing, fracture classification, BMI, diabetes mellitus, osteoporosis, and surgical reduction approach. All retained variables exhibited non-zero coefficients in the LASSO model. Incorporating the selected factors into the multivariable logistic regression revealed that age 29-58 years, BMI 18.5-27.9 kg/m², and early appropriate weight-bearing were protective factors for postoperative recovery following intramedullary nailing for femoral shaft fractures. Conversely, Type C fracture classification, diabetes mellitus, osteoporosis, and open reduction constituted independent risk factors for suboptimal postoperative recovery (Table 3). 3.3 Model verification and column chart construction As depicted in Figure 3 (ROC curve), the C-index for the training cohort was 0.895 (95% CI: 0.8654–0.9245), while the validation cohort achieved 0.7848 (95% CI: 0.6947–0.8749), confirming strong discriminative ability of the model. Calibration curves for the modeling group, internal validation group, and external validation group (Figure 4) demonstrated good agreement between predicted probabilities and actual outcomes. The close fit of prediction curves to the reference line indicates high consistency between predicted and observed risks of suboptimal postoperative recovery, validating the model's superior calibration performance. Per decision curve analysis (DCA) in Figure 5, when the threshold probability ranged from 0.19 to 1.00, clinical utility reached its optimum with more favorable net benefit from intervention implementation. The nomogram (Figures 6–7) serves as a visual prediction tool that transforms quantitative risk modeling results into intuitive graphical outputs. Clinicians can leverage this instrument to determine individualized risk probabilities of poor functional recovery following femoral shaft fracture surgery based on specific predictor profiles. An interactive web-based calculator has been developed using R Shiny, which allows users to directly access the webpage (https://femoralshaftfracture.shinyapps.io/DynNomapp1/) and input clinical characteristics to calculate the probability of poor postoperative recovery after intramedullary nailing for femoral shaft fractures and its 95% confidence interval (CI). For example, a patient aged 29-38 years with a B-type AO/OTA classification, BMI <18.5, no diabetes, no osteoporosis, and who underwent closed reduction surgery, can click 'predict' to automatically calculate that the probability of poor postoperative recovery is 0.038, with a 95% confidence interval of 0.005-0.218 (Figure 8). Table 1 Comparison of baseline characteristics between modeling group and verification group of femoral shaft fracture Variable Entire Cohort Training Cohort Validation Cohort χ2 Value P Value Functional scores Good 546 443 103 0.002 0.962 Suboptimal 126 102 24 Seasonal Categories Spring 158 117 41 7.255 0.064 Summer 189 156 33 Autumn 188 155 33 Winter 137 117 20 Actual length of hospital stay 0-7 37 28 9 2.387 0.303 8-14 252 199 53 >14 383 318 65 Age 18-28 17 14 3 1.888 0.966 29-38 90 72 18 39-48 128 101 27 49-58 95 77 18 59-68 125 100 25 69-78 126 103 23 79-88 56 48 8 >88 35 30 5 Occupation Student 15 12 3 2.098 0.835 Office clerk 77 61 16 Farmer 309 247 62 Manual workers 60 50 10 Retire 74 61 13 Other 134 114 20 Number of hospitalizations 1 647 525 122 1.653 0.648 2 20 15 5 3 4 4 0 >3 1 1 0 Waiting for surgery time 0-3 129 104 25 0.747 0.688 4-7 315 252 63 >7 228 189 39 Early weight-bearing Yes 198 159 39 0.117 0.733 No 474 386 88 AO/OTA typing A 216 170 46 4.087 0.130 B 282 225 57 C 174 150 24 BMI(kg/m2 ) <18.5 14 12 2 3.430 0.330 18.5–23.9 382 318 64 24–27.9 201 158 43 ≥ 28.0 75 57 18 Sex Male 455 365 90 0.714 0.398 Female 217 180 37 Marriage Single 60 46 14 6.363 0.095 Married 576 475 101 Widowed 9 6 3 Divorced 27 18 9 Nation Han 648 526 122 0.061 0.805 Other 24 19 5 Route of admission Emergency 476 381 95 1.195 0.274 Outpatient 196 164 32 Hypoproteinemia Yes 137 111 26 0.001 0.979 No 535 434 101 Diabetes mellitus Yes 75 65 10 1.706 0.191 No 597 480 117 Hypertension Yes 149 120 29 0.040 0.842 No 523 425 98 Coronary disease Yes 42 31 11 1.554 0.213 No 630 514 116 Osteoporosis Yes 40 32 8 0.034 0.854 No 632 513 119 Liver and gallbladder diseases Yes 84 62 22 3.330 0.068 No 588 483 105 Cause of injury Ground-level fall 208 172 36 0.858 0.931 Traffic accident 291 233 58 Fall from height 94 75 19 Direct trauma 70 58 12 Other 9 7 2 Drug allergy Yes 56 45 11 0.022 0.882 No 616 500 116 Blood group A 169 139 30 1.500 0.682 B 229 180 49 O 195 160 35 AB 79 66 13 Rh blood group Rh(+) 666 541 125 0.823 0.346 Rh(-) 6 4 2 Type of surgery Closed reduction 508 404 104 3.363 0.067 Open reduction 164 141 23 The level of surgery 1 3 3 0 1.609 0.657 2 85 71 14 3 516 414 102 4 68 57 11 Anesthesia Type General 231 184 47 3.573 0.311 Regional 87 66 21 Local 3 2 1 Combined 351 293 58 Total 672 545 127 Table 2 Coefficients of the factors selected by LASSO regression and one standard error of lambda Variable.Variable Variable.Coefficient lambda.1se Age (78–88 years) -0.3349154 0.04276359 Age (>88 years) -0.8337778 Early appropriate weight-bearing 0.6735787 AO/OTA typing (Type C) -0.5708824 BMI (>28 kg/m²) -0.8187149 Sex (Female) -0.0561418 Diabetes mellitus (DM) -0.2732441 Osteoporosis -1.9110700 Type of surgery (Open reduction) -1.1764856 Table 3. Results of multi-factor analysis of poor postoperative recovery after femoral shaft fracture B S.E, Wals Sig. Exp (B) Age 35.970 0.000 19-28 -20.461 9191.356 0.000 0.998 0.000 0.000 29-38 -2.751 0.815 11.400 0.001* 0.064 0.013-0.315 39-48 -1.732 0.639 7.343 0.007* 0.177 0.051-0.619 49-58 -1.943 0.674 8.325 0.004* 0.143 0.038-0.536 59-68 -0.824 0.575 2.058 0.151 0.439 0.142-1.352 69-78 -0.164 0.539 0.093 0.761 0.849 0.295-2.439 79-88 0.356 0.563 0.400 0.527 1.427 0.474-4.300 Early appropriate weight-bearing 0.711 0.298 5.689 0.017* 0.491 0.274-0.881 AO/OTA typing 23.857 0.000 Type B 0.392 0.333 1.384 0.239 1.480 0.770-2.844 Type C 1.479 0.329 20.239 0.000* 4.387 2.304-8.356 BMI 34.425 0.000 18.5–23.9 -1.618 0.748 4.674 0.031* 0.198 0.046-0.860 24–27.9 -1.748 0.777 5.060 0.024* 0.174 0.038-0.799 ≥ 28.0 0.355 0.793 0.200 0.654 1.426 0.301-6.751 Diabetes mellitus (DM) 1.054 0.331 10.118 0.001* 2.869 1.499-5.491 Osteoporosis 1.682 0.491 11.722 0.001* 5.377 2.053-14.085 Type of surgery 1.182 0.269 19.360 0.000 3.261 1.926-5.522 4. Discussion Femoral shaft fractures represent one of the most common long bone fractures resulting from high-energy trauma, for which accurate prediction of postoperative functional recovery holds significant clinical value in developing individualized treatment plans. This study employed LASSO regression for variable selection, successfully establishing and validating a predictive model for postoperative functional outcomes following intramedullary nailing of femoral shaft fractures, identifying critical determinants of recovery. Results demonstrated that age 29–58 years, BMI 18.5–27.9 kg/m², and early weight-bearing served as protective factors for recovery, while AO/OTA type C fractures, diabetes mellitus, osteoporosis, and open reduction constituted independent risk factors for suboptimal recovery. This study identified optimal recovery among patients aged 29–58 years, a finding closely aligned with recent high-quality research. Saul D et al. confirmed in their authoritative study that middle-aged populations possess ideal physiological conditions for bone healing, including optimal bone mineral density, adequate vascular supply networks, and active cellular regenerative capacity[ 28 ]. Court-Brown et al. supported this perspective through large-scale epidemiological research, noting higher fracture union rates in 30-60-year-old patients compared to other age groups[ 29 ]. This age stratum exhibits advantages including elevated osteocyte activity, appropriately regulated inflammatory responses, favorable vascularization, and fully functional mechanotransduction signaling pathways[ 30 – 31 ]. This study demonstrated significantly superior recovery among patients within the BMI range 18.5–27.9 kg/m², which fully aligns with contemporary evidence-based medical research. Farr JN et al. elucidated that moderate BMI levels effectively mitigate metabolic disorders and pro-inflammatory states caused by excessive adiposity[ 32 ]. Upadhyay J et al.'s multicenter prospective study[ 33 ] similarly confirmed that normal-to-mildly overweight BMI ranges optimize critical molecular events during bone healing, including enhanced osteoblast proliferation, neovascularization, and matrix mineralization processes[ 32 ]. Furthermore, appropriate body mass index provides essential mechanical loading that activates mechanosensitive ion channels on osteocyte membranes, stimulating Wnt signaling pathway activation and bone remodeling[ 34 ]. Contradicting traditional views, this investigation revealed that physiologically appropriate early weight-bearing actually facilitates postoperative recovery. Sankar et al.'s prospective RCT[ 35 ] definitively demonstrated that properly timed weight-bearing significantly accelerates fracture healing, whereas overly protective non-weight-bearing protocols prolong union time by approximately 20–30%. Robling et al.'s mechanistic research[ 36 – 37 ] comprehensively elucidated the central role of mechanical stimulation in bone healing, proving that moderate mechanical loading activates the essential Wnt/β-catenin signaling pathway to promote osteoblast differentiation and bone matrix formation. Early rational weight-bearing improves fracture-site perfusion and enhances nutrient delivery, thereby accelerating healing[ 38 ]. Fractures classified as AO/OTA Type C, representing comminuted fracture patterns, present well-documented challenges in healing extensively investigated in contemporary literature. Lin et al.'s [ 39 ] identified multiple fracture fragments and extensive soft tissue compromise as primary determinants impairing union. Santolini et al.'s [ 13 ] stratified analysis revealed 3-4-fold higher nonunion rates versus simple fractures, attributable to reduced fracture surface contact, compromised vascularization, and mechanical instability. Massari et al.'s [ 40 – 41 ] large-scale epidemiological investigation established significant positive correlation between fracture line complexity and prolonged consolidation periods. The multiple negative impacts of diabetes on bone healing have been extensively confirmed by numerous high-quality studies. Jiao et al. [ 42 ] found that the bone healing time for diabetic patients is extended by 40–87%, and the incidence of complications increases by 2-3.4 times. Further research into the mechanisms reveals that chronic hyperglycemia affects fracture healing[ 43 – 44 ] by directly inhibiting osteoblast proliferation and differentiation, damaging vascular endothelial cell function, and increasing oxidative stress. Chattopadhyay et al. [ 19 , 20 ] molecular biology studies found that the levels of key bone morphogenetic proteins (BMP-2, BMP-7) in the serum of diabetic patients are significantly reduced, severely impacting the initiation and maintenance of bone regeneration. Osteoporosis impairs fracture healing through multifaceted disruption of bone microstructure and cellular functionality. Gorter et al.'s big-data analysis demonstrated significantly higher healing failure rates among osteoporosis patients versus healthy controls[ 45 – 46 ]. Evidence confirms BMD T-score as a potent independent predictor of adverse healing outcomes[ 47 – 48 ]. Pathological alterations including trabecular microstructure deterioration, decreased osteocyte density, and impaired bone matrix protein synthesis substantially compromise healing efficiency in osteoporosis patients[ 49 ]. Closed reduction combined with intramedullary nailing yields superior outcomes versus open reduction in union rates, nonunion incidence, and infection rates. Open reduction necessitates extensive soft-tissue dissection, inevitably disrupting peri-fracture vascular networks and periosteal integrity. The periosteum, a critical structure containing rich osteoprogenitor cells and vascular plexuses, requires preservation; its removal or damage diminishes fracture-site perfusion and significantly impairs osteogenic regenerative capacity[ 50 – 51 ]. Chen et al. report heightened infection risks with open procedures, a major nonunion risk factor [ 52 ]. Korytkowski et al.'s meta-analytic study confirmed closed reduction's superiority over open reduction across union success rates, healing timeframes, and overall infection incidence[ 53 ]. In summary, this investigation identified seven independent determinants significantly influencing recovery: age, BMI, early appropriate weight-bearing, fracture classification, diabetes mellitus, osteoporosis, and surgical approach. The developed model provides clinicians with quantified individual risk assessment during preoperative or early postoperative phases, supporting formulation of precise therapeutic strategies. By identifying high-risk patients, targeted interventions, such as optimizing glycemic control, intensifying anti-osteoporosis therapy, and implementing personalized rehabilitation, may substantially improve prognostic outlooks and enhance quality of life[ 54 ]. This study effectively addressed the multicollinearity issue in high-dimensional data using the LASSO regression method, avoiding the risk of overfitting that can occur with traditional regression analysis. Additionally, the model's robustness was ensured through both internal and external validation. Furthermore, the study included a wide range of predictive variables, such as patient demographics, fracture characteristics, and surgical factors, to construct a comprehensive predictive system. Ultimately, the webpage we designed provides convenience for patients and doctors, enabling personalized postoperative outcome expectations for patients undergoing intramedullary nailing treatment for femoral shaft fractures. However, this study has certain limitations. As a single-center retrospective study, it lacks representativeness, which may introduce selection bias and information bias. Future studies should conduct multi-center prospective validation, including a more diverse patient population, to build a more comprehensive and precise predictive model. 5. Conclusion This study successfully developed and validated a predictive model for postoperative functional outcomes following intramedullary nailing of femoral shaft fractures. Key determinants include protective factors: age 29-58 years, BMI 18.5-27.9 kg/m², and early appropriate weight-bearing; alongside independent risk factors: AO/OTA type C fractures, diabetes mellitus, osteoporosis, and open reduction. The model demonstrates robust discriminative ability and high calibration accuracy, providing a quantitative clinical tool for individualizing treatment strategies and guiding precision rehabilitation. This framework shows significant potential for optimizing patient prognoses and enhancing healthcare service quality in orthopedic trauma management. Abbreviations IMN Intramedullary Nailing LASSO Least Absolute Shrinkage and Selection Operator DM Diabetes Mellitus H-L testing Hosmer-Lemeshow testing DCA Decision Curve Analysis ROC Receiver Operating Characteristic BMI Body Mass Index SE The Standard Error OR The Odds Ratio Declarations Ethics approval and consent to participate Our study obtained approval from the Ethics Committee ofthe Third Hospital of Hebei Medical University, with approval number K2015-002-1. Every human participant has provided their consent. Consent for publication All participants have agreed to the publication of any relevant images in this paper. Availability of data and materials The datasets used or analyzed during the current study are available from the corresponding author on reasonable request. Competing Interests All authors declare that they have no competing interests. Funding This work was supported by the Key Projects of the National Natural Science Foundation of China (Grant No. 32130052), and Natural Science Foundation of Hebei Province (CN) - for Yanzhao Young Scientists Project (Grant No. H2023206519). Author contributions T.Z., G.Z.H., and Q.X. contributed equally to the study. W.C., and H.Z.L. designed the study, supervised the experiments and manuscript. T.Z., G.Z.H., and Q.X. performed the experiments and wrote the manuscript. Y.Z.Z. and Z.Y.H. provided technical support, reviewed the manuscript and made significant revisions. All the authors have read and approved the final version of the manuscript. Acknowledgements We thank each institution and each researcher who contributed to this article. References Court-Brown CM, Caesar B. Epidemiology of adult fractures: A review. Injury. 2006 Aug;37(8):691-7. Brumback RJ, Jones AL. Interobserver agreement in the classification of open fractures of the tibia. 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EClinicalMedicine. 2021 Oct 31;42:101179. Li AB, Zhang WJ, Guo WJ, Wang XH, Jin HM, Zhao YM. Reamed versus unreamed intramedullary nailing for the treatment of femoral fractures: A meta-analysis of prospective randomized controlled trials. Medicine (Baltimore). 2016 Jul;95(29):e4248. Hendrickx LAM, Virgin J, van den Bekerom MPJ, Doornberg JN, Kerkhoffs GMMJ, Jaarsma RL. Complications and subsequent surgery after intra-medullary nailing for tibial shaft fractures: Review of 8110 patients. Injury. 2020 Jul;51(7):1647-1654. Kong SH, Ahn D, Kim BR, Srinivasan K, Ram S, Kim H, Hong AR, Kim JH, Cho NH, Shin CS. A Novel Fracture Prediction Model Using Machine Learning in a Community-Based Cohort. JBMR Plus. Kawasaki N, Takegami Y, Sakai R, Todoroki K, Uemi R, Imagama S; Hospitals of Trauma Research of Nagoya (TRON) group. Prediction of delayed union of tibial shaft fracture treated with intramedullary nailing: multicenter-study analysis and literature review -the TRON study. Eur J Orthop Surg Traumatol. 2022 Jan;32(1):129-135. Prediction of femoral osteoporosis using machine-learning analysis with radiomics features and abdomen-pelvic CT: A retrospective single center preliminary study.PLoS One. 2021 Mar 4;16(3):e0247330. Chitwood JR, Chakraborty N, Hammamieh R, Moe SM, Chen NX, Kacena MA, Natoli RM. Predicting fracture healing with blood biomarkers: the potential to assess patient risk of fracture nonunion. Biomarkers. 2021 Dec;26(8):703-717. Saul D, Khosla S. Fracture Healing in the Setting of Endocrine Diseases, Aging, and Cellular Senescence. Endocr Rev. 2022 Nov 25;43(6):984-1002. Court-Brown CM, Caesar B. Epidemiology of adult fractures: A review. Injury. 2006 Aug;37(8):691-7. Castillo AB, Chen JC, Jacobs CR. Cellular and molecular mechanotransduction in bone. Academic Press. 2021:309-335. Rubin J, Rubin C, Jacobs CR. Molecular pathways mediating mechanical signaling in bone. Gene. 2006 Feb 15;367:1-16. Farr JN, Khosla S. Skeletal changes through the lifespan—from growth to senescence. Nature Reviews Endocrinology. 2015;11(9):513-521. Upadhyay J, Farr OM, Mantzoros CS. The role of leptin in regulating bone metabolism. Metabolism. 2015 Jan;64(1):105-13. Robling AG, Bonewald LF. The osteocyte: new insights. Annual Review of Physiology. 2020;82:485-506. Sankar, Wudbhav N. MD1; Nduaguba, Afamefuna BS1; Flynn, John M. MD1. Ilfeld Abduction Orthosis Is an Effective Second-Line Treatment After Failure of Pavlik Harness for Infants with Developmental Dysplasia of the Hip. The Journal of Bone and Joint Surgery 97(4):p 292-297, February 18, 2015. Robling AG, Bonewald LF. The Osteocyte: New Insights. Annu Rev Physiol. 2020 Feb 10;82:485-506. Kang KS, Robling AG. New Insights into Wnt-Lrp5/6-β-Catenin Signaling in Mechanotransduction. Front Endocrinol (Lausanne). 2015 Jan 20;5:246. Meyers N, Schülke J, Ignatius A, Claes L. Novel systems for the application of isolated tensile, compressive, and shearing stimulation of distraction callus tissue. PLoS One. 2017 Dec 11;12(12):e0189432. Lin SJ, Chen CL, Peng KT, Hsu WH. Effect of fragmentary displacement and morphology in the treatment of comminuted femoral shaft fractures with an intramedullary nail. Injury. 2014 Apr;45(4):752-6. Massari L, Falez F, Lorusso V, Zanon G, Ciolli L, La Cava F, Cadossi M, Chiarello E, De Terlizzi F, Setti S, Benazzo FM. Can a combination of different risk factors be correlated with leg fracture healing time? J Orthop Traumatol. 2013 Mar;14(1):51-7. Court-Brown CM, Bugler KE, Clement ND, Duckworth AD, McQueen MM. The epidemiology of open fractures in adults. A 15-year review. Injury. 2012 Jun;43(6):891-7. Jiao H, Xiao E, Graves DT. Diabetes and Its Effect on Bone and Fracture Healing. Curr Osteoporos Rep. 2015 Oct;13(5):327-35. Marin C, Luyten FP, Van der Schueren B, Kerckhofs G, Vandamme K. The Impact of Type 2 Diabetes on Bone Fracture Healing. Front Endocrinol (Lausanne). 2018 Jan 24;9:6. Chen Y, Zhou Y, Lin J, Zhang S. Challenges to Improve Bone Healing Under Diabetic Conditions. Front Endocrinol (Lausanne). 2022 Mar 28;13:861878. Gorter EA, Reinders CR, Krijnen P, Appelman-Dijkstra NM, Schipper IB. The effect of osteoporosis and its treatment on fracture healing a systematic review of animal and clinical studies. Bone Rep. 2021 Aug 16;15:101117. Orji C, Ojo C, Onobun DE, Igbokwe K, Khaliq F, Ononye R. Fracture Non-Union in Osteoporotic Bones: Current Practice and Future Directions. Cureus. 2024 Sep 20;16(9):e69778. Shevroja E, Cafarelli FP, Guglielmi G, Hans D. DXA parameters, Trabecular Bone Score (TBS) and Bone Mineral Density (BMD), in fracture risk prediction in endocrine-mediated secondary osteoporosis. Endocrine. 2021 Oct;74(1):20-28. Jackuliak P, Payer J. Osteoporosis, fractures, and diabetes. Int J Endocrinol. 2014;2014:820615. Fischer V, Haffner-Luntzer M, Amling M, Ignatius A. Calcium and vitamin D in bone fracture healing and post-traumatic bone turnover. Eur Cell Mater. 2018 Jun 22;35:365-385. Salman LA, Al-Ani A, Radi MFA, Abudalou AF, Baroudi OM, Ajaj AA, Alkhayarin M, Ahmed G. Open versus closed intramedullary nailing of femur shaft fractures in adults: a systematic review and meta-analysis. Int Orthop. 2023 Dec;47(12):3031-3041. Tahririan MA, Andalib A. Is there a place for open intramedullary nailing in femoral shaft fractures? Adv Biomed Res. 2014 Jul 31;3:157. Chen YH, Liao HJ, Lin SM, Chang CH, Rwei SP, Lan TY. Radiographic outcomes of the treatment of complex femoral shaft fractures (AO/OTA 32-C) with intramedullary nailing: a retrospective analysis of different techniques. J Int Med Res. 2022 Jun;50(6):3000605221103974. Korytkowski PD, Panzone JM, Aldahamsheh O, Mubarak Alkhayarin M, Omar Almohamad H, Alhammoud A. Open and closed reduction methods for intramedullary nailing of femoral shaft fractures: A systematic review and meta-analysis of comparative studies. J Clin Orthop Trauma. 2023 Sep 22;44:102256. Tu JB, Shi C, Zhang Y, et al. Using machine learning techniques to predict the risk of osteoporosis and fractures: A systematic review. Scientific Reports. 2024;14:5114. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 08 Oct, 2025 Reviews received at journal 07 Oct, 2025 Reviewers agreed at journal 24 Sep, 2025 Reviews received at journal 13 Sep, 2025 Reviewers agreed at journal 03 Sep, 2025 Reviewers invited by journal 25 Jul, 2025 Editor invited by journal 24 Jul, 2025 Editor assigned by journal 18 Jul, 2025 Submission checks completed at journal 17 Jul, 2025 First submitted to journal 17 Jul, 2025 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. 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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-7080815","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":491062983,"identity":"f17e53dd-67a4-41c7-aa27-cba12213450f","order_by":0,"name":"Tao Zhang","email":"","orcid":"","institution":"Department of Orthopaedic Surgery, Hebei Medical University Third Hospital","correspondingAuthor":false,"prefix":"","firstName":"Tao","middleName":"","lastName":"Zhang","suffix":""},{"id":491062984,"identity":"d9092418-2f0a-44e2-a956-21b2a1e17221","order_by":1,"name":"Guangzhao Hou","email":"","orcid":"","institution":"Department of Orthopaedic Surgery, Hebei Medical University Third Hospital","correspondingAuthor":false,"prefix":"","firstName":"Guangzhao","middleName":"","lastName":"Hou","suffix":""},{"id":491062985,"identity":"c7cc6d22-22ba-4490-98d5-6a36de571517","order_by":2,"name":"Qian Xiao","email":"","orcid":"","institution":"Department of Orthopaedic Surgery, Hebei Medical University Third Hospital","correspondingAuthor":false,"prefix":"","firstName":"Qian","middleName":"","lastName":"Xiao","suffix":""},{"id":491062986,"identity":"fd7f7c3e-d747-42ee-ad48-550646c92560","order_by":3,"name":"Zhiyong Hou","email":"","orcid":"","institution":"Department of Orthopaedic Surgery, Hebei Medical University Third Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhiyong","middleName":"","lastName":"Hou","suffix":""},{"id":491062988,"identity":"d8b1bb27-5e3a-4480-816a-ae0caa8186fa","order_by":4,"name":"Yingze Zhang","email":"","orcid":"","institution":"Department of Orthopaedic Surgery, Hebei Medical University Third Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yingze","middleName":"","lastName":"Zhang","suffix":""},{"id":491062991,"identity":"67934d05-1ce4-4814-b1b6-15f0a062a7ee","order_by":5,"name":"Hongzhi Lv","email":"","orcid":"","institution":"Department of Orthopaedic Surgery, Hebei Medical University Third Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hongzhi","middleName":"","lastName":"Lv","suffix":""},{"id":491062992,"identity":"baf04a70-37b2-4d7b-a369-8b9ba55b574e","order_by":6,"name":"Wei Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8UlEQVRIiWNgGAWjYDADAwhpw8PP3kCaljQZyZ4DJGlhOGxjcMMBv0r5GbmPX3youCNvzt57TOJDwXkehhsMjB8+5uAx/Ea6meWMM88Md/acS5OcYXCbh3F2A7PkzG14tEiksRnzth1OMLiRY3abB6iFWeYAGzMvHi3yM4Ba/oK03H9jdvuPwTkeNokE/FoYbqQxP2YE28JjdpvB4AAPDyEtBmeesTH2nDlsuOFMjvnPHoNkHgmeg814/SLfnsb84UfFYXmD42eMDX78sbO3P9588MNHfA5jYGCTQBNgbMCrHgiYPxBSMQpGwSgYBSMcAABHfFKkdv5+cwAAAABJRU5ErkJggg==","orcid":"","institution":"Department of Orthopaedic Surgery, Hebei Medical University Third Hospital","correspondingAuthor":true,"prefix":"","firstName":"Wei","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2025-07-09 07:08:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7080815/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7080815/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87885977,"identity":"4879fc2f-fe6b-46fe-8039-10ff18e9478a","added_by":"auto","created_at":"2025-07-30 05:10:56","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":88036,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of inclusion and exclusion criteria for patients with femoral shaft fractures.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7080815/v1/4d11a85f9aa098a568ee4e19.png"},{"id":87883824,"identity":"e68ffd6e-3d12-4407-9c4a-2f2697ca0054","added_by":"auto","created_at":"2025-07-30 04:54:56","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":101479,"visible":true,"origin":"","legend":"\u003cp\u003eA: LASSO regression coefficient profile diagram, showing the order of coefficients of each predictor variable as the regularization parameter (lambda) increases. B: LASSO regression coefficient curve: the trend of coefficient changes for different predictor variables as the regularization parameter (lambda) increases, with each curve representing a different predictor variable in the LASSO regression model.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7080815/v1/901651af3ed5273ac7f0314c.png"},{"id":87884892,"identity":"20ae18bb-8f9d-4ff9-97fd-d8b7eb0f9bc5","added_by":"auto","created_at":"2025-07-30 05:02:56","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":93543,"visible":true,"origin":"","legend":"\u003cp\u003eROC curves for the prediction model of poor recovery after femoral shaft fracture surgery (left panel: training group; right panel: validation group).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7080815/v1/1375628db32e3a74cfaa2b48.png"},{"id":87885979,"identity":"45dbc7d9-8437-44b3-af3f-07e715c98e71","added_by":"auto","created_at":"2025-07-30 05:10:56","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":74509,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration curves for poor recovery after femoral shaft fracture surgery (left panel: training group; middle panel: validation group; right panel: bootstrap internal validation).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7080815/v1/dc343daf61fd393e4b298df2.png"},{"id":87883822,"identity":"0767e257-4f39-45fd-ac51-5e1ed42e13da","added_by":"auto","created_at":"2025-07-30 04:54:56","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":57362,"visible":true,"origin":"","legend":"\u003cp\u003eDCA curves for poor recovery after femoral shaft fracture surgery (left panel: training group; right panel: validation group).\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7080815/v1/15baaff914f53d0616bb9ecd.png"},{"id":87883836,"identity":"c766ac86-48ff-42f3-bf74-d8cde49e69a1","added_by":"auto","created_at":"2025-07-30 04:54:57","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":76535,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram for poor recovery after femoral shaft fracture surgery.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7080815/v1/e5b67befc755b1c30215fdc5.png"},{"id":87883831,"identity":"c7d7b110-f191-41f6-b5a6-54cf80cf0552","added_by":"auto","created_at":"2025-07-30 04:54:56","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":105943,"visible":true,"origin":"","legend":"\u003cp\u003eInteractive nomogram for poor recovery after femoral shaft fracture surgery.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-7080815/v1/79a6cf784276ea0725ce7dac.png"},{"id":87883830,"identity":"8e6f2632-3465-45ad-ab98-3825947e6e8b","added_by":"auto","created_at":"2025-07-30 04:54:56","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":43114,"visible":true,"origin":"","legend":"\u003cp\u003eExample of interactive web-based calculator application based on R shiny (postoperative recovery probability under different conditions). https://femoralshaftfracture.shinyapps.io/DynNomapp1/\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-7080815/v1/4b1cbcd5b183496ccb8e781c.png"},{"id":88505274,"identity":"e3b9e36f-db9f-49dc-bea5-b3c15c9e7f4d","added_by":"auto","created_at":"2025-08-07 07:22:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1421318,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7080815/v1/56b33229-8806-4574-801c-a2698f253ad8.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A novel web-based online nomogram was used to predict postoperative function in patients with femoral shaft fractures treated with intramedullary nails","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eFemoral shaft fractures are among the relatively common high-energy trauma-induced long bone fractures in adults, accounting for 3\u0026ndash;6% of all fractures[\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In Western Europe and North America, the annual incidence approximates 10\u0026ndash;21 per 100,000 people[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], while domestic data in China report an incidence of 19 per 100,000 people, though these rely solely on statistics for pediatric femoral shaft fractures[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Closed femoral shaft fractures, characterized by relatively mild soft tissue injury and lower risk of infection, are considered to have superior prognosis compared to open fractures, but still retain challenges for postoperative healing[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSince Kuntscher introduced the intramedullary nailing (IMN) technique in the 1940s, closed reduction combined with interlocking intramedullary nailing has become the internationally recognized preferred treatment[\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. IMN fixation, via a minimally invasive approach, preserves the peri-fracture soft tissue blood supply while providing biomechanical stability aligned with the femoral axis, enabling early weight-bearing. Furthermore, it demonstrates significantly lower complication rates compared to plate fixation[\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Currently, IMN constitutes over 80% of surgical interventions for femoral shaft fractures in European and North American trauma centers[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], and has emerged as the primary surgical approach for such fractures in most Class A tertiary hospitals in China[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. However, clinical practice reveals substantial variations in outcomes among patients receiving standardized IMN procedures, with a relatively high incidence of delayed union or nonunion[\u003cspan additionalcitationids=\"CR17 CR18\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Postoperative prognosis is collectively influenced by multiple factors, including patient-specific conditions, fracture classification, and surgical techniques[\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. At present, orthopedic surgeons primarily rely on clinical experience for risk assessment, lacking quantifiable and generalizable predictive tools[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eNevertheless, recent predictive models for femoral fracture healing or complications exhibit significant limitations: inadequate sample sizes and insufficient predictor diversity result in limited model robustness[\u003cspan additionalcitationids=\"CR25 CR26\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Consequently, developing a multidimensional prediction tool, internally and externally validated, holds substantial clinical value for postoperative risk stratification in closed femoral shaft fractures. This approach may reduce suboptimal recovery rates, provide evidence-based decision support for clinicians, improve patient prognosis, and enhance quality of life.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Data Collection\u003c/h2\u003e\u003cp\u003e This retrospective study complies with the principles of the Declaration of Helsinki. The study population consisted of patients who underwent treatment for femoral shaft fractures at the Third Hospital of Hebei Medical University between January 1, 2019, and December 31, 2023. All basic clinical data were obtained from the hospital's medical records, and all patients provided informed consent. The study design was approved by the Ethics Committee of the Hebei Medical University Third Hospital (Approval Number: K2015-002-1). Clinical trial number: not applicable.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Inclusion and Exclusion Criteria\u003c/h2\u003e\u003cp\u003eInclusion criteria: 1) Unilateral closed femoral shaft fractures; 2) IMN via open or closed reduction techniques; 3) Age\u0026thinsp;\u0026gt;\u0026thinsp;18 years; 4) Complete medical records and imaging data availability. Exclusion criteria: 1) Multiple ipsilateral lower limb fractures; 2) Fractures complicated by neurovascular injuries; 3) Pathological fractures, congenital deformities, or other diseases impairing lower limb function; 4) Non-acute fractures (\u0026gt;\u0026thinsp;3 weeks post-injury); 5) Severe systemic diseases (cardiac/cerebral/pulmonary) contraindicated for surgical intervention; 6) Psychiatric disorders impeding treatment compliance.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Predictor Variables\u003c/h2\u003e\u003cp\u003eData were collected via telephone follow-up and medical record retrieval: (1) preoperative factors: gender, age, marriage, nationality, occupation, body mass index (BMI), season, AO/OTA classification, injury cause, preoperative basic diseases (hypocalcemia, diabetes, hypertension, coronary heart disease, osteoporosis, liver and biliary system diseases, etc.). (2) intraoperative factors: time-to-surgery, surgical mode (Open vs. closed reduction). (3) postoperative factors: time-to-weight-bearing initiation, hospital length of stay. Patients were divided into eight age groups: 19\u0026ndash;28, 29\u0026ndash;38, 39\u0026ndash;48, 49\u0026ndash;58, 59\u0026ndash;68, 69\u0026ndash;78, 79\u0026ndash;88, and \u0026ge;\u0026thinsp;89 years. According to the AO/OTA classification, fractures were classified as type A, B and C, which corresponded to simple, wedge and comminuted fractures, respectively. Early weight-bearing: Mobilization initiated on postoperative day 3 under physiotherapist guidance. Began with partial weight-bearing using assistive devices (e.g., crutches). Progressed to full weight-bearing as tolerated.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Study Outcomes\u003c/h2\u003e\u003cp\u003ePostoperative recovery was evaluated using the Lower Extremity Functional Scale (LEFS) developed by Binkley et al., combined with radiographic assessment. LEFS demonstrates high clinical feasibility for quantitative functional assessment. LEFS score\u0026thinsp;\u0026gt;\u0026thinsp;70 points defined good functional recovery. Delayed union: Radiographic evidence of detectable but insufficient callus formation with persistent fracture line visibility at 4\u0026ndash;8 months postoperatively, without sclerotic changes. Nonunion: Failure of healing with sclerotic fracture ends and bone resorption at 8\u0026ndash;12 months postoperatively. Cases meeting criteria for delayed union or nonunion were classified as failure of fracture healing.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Statistical Analysis\u003c/h2\u003e\u003cp\u003eStatistical analyses were performed using R software version 4.3.0. All variables were converted into categorical variables, and frequency and proportion were used to describe the data. Chi-square test was used for inter-group comparisons; when the theoretical frequency of any cell in a 2\u0026times;2 contingency table is less than 1 or the theoretical frequency of more than 20% cells in an R\u0026times;C contingency table is less than 5, Fisher's exact test was applied. Patients from 2021 were randomly assigned to the validation cohort, while patients from other years were assigned to the training cohort. LASSO regression was used to reduce the dimensionality of the data, avoiding overfitting and ensuring model accuracy, to identify factors affecting postoperative functional outcomes. A 10-fold cross-validation was performed, and the optimal value was determined by the 1-standard error λ. Based on the characteristics of LASSO regression, multiple logistic regression was used to further analyze the variables and identify independent risk factors for poor recovery after femoral shaft fractures. In the logistic regression analysis table, beta (β) represents the regression coefficient, indicating the impact of the independent variable on the dependent variable in the regression equation. The standard error (SE) measures sampling error; the smaller the SE, the higher the reliability of inferring population parameters from sample statistics. The odds ratio (OR) reflects the correlation between the disease and exposure. A positive value indicates a positive correlation, while a negative value indicates a negative correlation, with the magnitude reflecting the strength of the correlation. The confidence interval (CI) is the interval of the estimated population parameter constructed using the sample statistic. The CI in the table is the confidence interval of the OR value.\u003c/p\u003e\u003cp\u003eThe final predictors identified through multivariable analysis were incorporated into a nomogram model predicting suboptimal recovery after closed femoral shaft fracture surgery. In the training cohort, we conducted internal validation using the bootstrap resampling process (n\u0026thinsp;=\u0026thinsp;1000) and plotted calibration curves to assess the model's predictive accuracy. The ROC curve was used to evaluate the model's efficiency; a higher AUC indicates better predictive power. The DCA curve was also plotted to assess clinical effectiveness and to obtain net clinical benefit. The R Shiny package, an open-source toolkit for developing data-driven visualizations, was employed to construct an interactive web application framework that visually presents functional recovery outcomes following intramedullary nailing for femoral shaft fractures, with integrated automated risk calculation capabilities. All statistical tests were performed with two-tailed tests, and a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e3.1 Cohort Characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 1\u0026nbsp;illustrates participant inclusion/exclusion and group allocation. This study enrolled 794 eligible participants between January 2019 and December 2013; among them, 12 patients had ipsilateral lower extremity multiple fractures, 1 patient presented with a pathological fracture, and 4 patients had non-acute fractures. After 105 cases were lost to follow-up during the postoperative one-year period, 672 patients were included in the cohort for analysis following exclusions. Patients treated in 2021 were randomly assigned to the validation cohort, while those treated in other years formed the training cohort. Baseline characteristics between cohorts were comparable (P\u0026gt;0.05). Based on Schatzker-Lambert scoring and radiographic evaluation, 546 patients (81.2%) achieved good scores and showed satisfactory healing, whereas 126 patients (18.8%) had suboptimal scores or nonunion. The mean age was 50.22 \u0026plusmn; 17.73 years, with 455 males (67.70%) and 217 females (32.30%), yielding a male-to-female ratio of 2.10:1. The 39-48 age group had the highest representation (128 cases, 19.04%). Regarding occupation, farmers constituted 309 cases (46.00%), BMI 18.5-23.9 kg/m\u0026sup2;\u0026nbsp;comprised 382 cases (56.85%), blood type B accounted for 229 cases (34.08%), hospitalization \u0026gt;14 days included 383 cases (57.00%), and summer admissions totaled 189 cases (28.12%). Detailed data are presented in\u0026nbsp;Table 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Variable selection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo prevent overfitting, LASSO regression was employed for variable selection\u0026nbsp;(Figure 2). This was executed using the\u0026nbsp;glmnet package in R, combined with a\u0026nbsp;10-fold cross-validation strategy\u0026nbsp;to determine the optimal regularization parameter (\u0026lambda;). For a more parsimonious model, the \u0026lambda; value corresponding to\u0026nbsp;one standard error\u0026nbsp;was selected\u0026nbsp;(Table 2). After screening, the collected variables were reduced to\u0026nbsp;eight predictors: gender, age, early weight-bearing, fracture classification, BMI, diabetes mellitus, osteoporosis, and surgical reduction approach. All retained variables exhibited\u0026nbsp;non-zero coefficients\u0026nbsp;in the LASSO model.\u003c/p\u003e\n\u003cp\u003eIncorporating the selected factors into the multivariable logistic regression revealed that age 29-58 years, BMI 18.5-27.9 kg/m\u0026sup2;, and early appropriate weight-bearing were protective factors for postoperative recovery following intramedullary nailing for femoral shaft fractures. Conversely, Type C fracture classification, diabetes mellitus, osteoporosis, and open reduction constituted independent risk factors for suboptimal postoperative recovery\u0026nbsp;(Table 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Model verification and column chart construction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs depicted in\u0026nbsp;Figure 3\u0026nbsp;(ROC curve), the C-index for the training cohort was 0.895 (95% CI: 0.8654\u0026ndash;0.9245), while the validation cohort achieved 0.7848 (95% CI: 0.6947\u0026ndash;0.8749), confirming strong discriminative ability of the model. Calibration curves for the modeling group, internal validation group, and external validation group\u0026nbsp;(Figure 4)\u0026nbsp;demonstrated\u0026nbsp;good agreement\u0026nbsp;between predicted probabilities and actual outcomes. The\u0026nbsp;close fit\u0026nbsp;of prediction curves to the reference line indicates high consistency between predicted and observed risks of suboptimal postoperative recovery, validating the model\u0026apos;s\u0026nbsp;superior calibration performance.\u003c/p\u003e\n\u003cp\u003ePer decision curve analysis (DCA) in\u0026nbsp;Figure 5, when the threshold probability ranged from 0.19 to 1.00, clinical utility reached its optimum with more favorable net benefit from intervention implementation. The nomogram\u0026nbsp;(Figures 6\u0026ndash;7)\u0026nbsp;serves as a visual prediction tool that transforms quantitative risk modeling results into intuitive graphical outputs. Clinicians can leverage this instrument to determine individualized risk probabilities of poor functional recovery following femoral shaft fracture surgery based on specific predictor profiles.\u003c/p\u003e\n\u003cp\u003eAn interactive web-based calculator has been developed using R Shiny, which allows users to directly access the webpage (https://femoralshaftfracture.shinyapps.io/DynNomapp1/) and input clinical characteristics to calculate the probability of poor postoperative recovery after intramedullary nailing for femoral shaft fractures and its 95% confidence interval (CI). For example, a patient aged 29-38 years with a B-type AO/OTA classification, BMI \u0026lt;18.5, no diabetes, no osteoporosis, and who underwent closed reduction surgery, can click \u0026apos;predict\u0026apos; to automatically calculate that the probability of poor postoperative recovery is 0.038, with a 95% confidence interval of 0.005-0.218 (Figure 8).\u003c/p\u003e\n\u003cp\u003eTable 1 Comparison of baseline characteristics between modeling group and verification group of femoral shaft fracture\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003eEntire Cohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eTraining Cohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eValidation Cohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026chi;2 Value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eP Value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 568px;\"\u003e\n \u003cp\u003eFunctional scores\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e546\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e443\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e0.962\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eSuboptimal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 568px;\"\u003e\n \u003cp\u003eSeasonal Categories\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eSpring\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e7.255\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eSummer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eAutumn\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eWinter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" style=\"width: 568px;\"\u003e\n \u003cp\u003eActual length of hospital stay\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e0-7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e2.387\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e0.303\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e8-14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e252\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e\u0026gt;14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e383\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e318\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" style=\"width: 568px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e18-28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"8\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e1.888\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"8\" valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e0.966\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e29-38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e39-48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e49-58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e59-68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e69-78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e79-88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e\u0026gt;88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" style=\"width: 568px;\"\u003e\n \u003cp\u003eOccupation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eStudent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"6\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e2.098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"6\" valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e0.835\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eOffice clerk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eFarmer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e309\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e247\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eManual workers\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eRetire\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" style=\"width: 568px;\"\u003e\n \u003cp\u003eNumber of hospitalizations\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e647\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e525\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e1.653\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e0.648\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e\u0026gt;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" style=\"width: 568px;\"\u003e\n \u003cp\u003eWaiting for surgery time\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e0-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.747\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e0.688\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e4-7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e315\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e252\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e\u0026gt;7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" style=\"width: 568px;\"\u003e\n \u003cp\u003eEarly weight-bearing\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e0.733\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e474\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e386\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e88\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 568px;\"\u003e\n \u003cp\u003eAO/OTA typing\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e216\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e4.087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e0.130\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e282\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" style=\"width: 568px;\"\u003e\n \u003cp\u003eBMI(kg/m2 )\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e\u0026lt;18.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e3.430\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e0.330\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e18.5\u0026ndash;23.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e382\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e318\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e24\u0026ndash;27.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e\u0026ge; 28.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" style=\"width: 568px;\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e455\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.714\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e0.398\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e217\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 568px;\"\u003e\n \u003cp\u003eMarriage\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eSingle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e6.363\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e0.095\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e576\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e475\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e101\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eWidowed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eDivorced\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" style=\"width: 568px;\"\u003e\n \u003cp\u003eNation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eHan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e648\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e526\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e0.805\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 568px;\"\u003e\n \u003cp\u003eRoute of admission\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eEmergency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e476\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e381\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e1.195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e0.274\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eOutpatient\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 568px;\"\u003e\n \u003cp\u003eHypoproteinemia\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e0.979\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e535\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e434\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e101\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 568px;\"\u003e\n \u003cp\u003eDiabetes mellitus\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e1.706\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e0.191\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e597\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e480\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e117\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 568px;\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e0.842\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e523\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e425\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 568px;\"\u003e\n \u003cp\u003eCoronary disease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e1.554\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e0.213\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e630\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e514\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e116\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" style=\"width: 568px;\"\u003e\n \u003cp\u003eOsteoporosis\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e0.854\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e632\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e513\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e119\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 568px;\"\u003e\n \u003cp\u003eLiver and gallbladder diseases\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e3.330\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e0.068\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e588\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e483\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e105\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 568px;\"\u003e\n \u003cp\u003eCause of injury\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eGround-level fall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.858\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e0.931\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eTraffic accident\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e291\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eFall from height\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eDirect trauma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" style=\"width: 568px;\"\u003e\n \u003cp\u003eDrug allergy\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e0.882\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e616\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e116\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" style=\"width: 568px;\"\u003e\n \u003cp\u003eBlood group\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e1.500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e0.682\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eAB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" style=\"width: 568px;\"\u003e\n \u003cp\u003eRh blood group\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003eRh(+)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e666\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e541\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.823\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e0.346\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003eRh(-)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" style=\"width: 568px;\"\u003e\n \u003cp\u003eType of surgery\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003eClosed reduction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e508\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e404\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e3.363\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e0.067\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003eOpen reduction\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" style=\"width: 568px;\"\u003e\n \u003cp\u003eThe level of surgery\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e1.609\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e0.657\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e516\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e414\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e102\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" style=\"width: 568px;\"\u003e\n \u003cp\u003eAnesthesia Type\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003eGeneral\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e3.573\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e0.311\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003eRegional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003eLocal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003eCombined\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e351\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e293\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e672\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e545\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;Table 2 Coefficients of the factors selected by LASSO regression and one standard error of lambda\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"569\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003eVariable.Variable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 187px;\"\u003e\n \u003cp\u003eVariable.Coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 166px;\"\u003e\n \u003cp\u003elambda.1se\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003eAge (78\u0026ndash;88 years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 187px;\"\u003e\n \u003cp\u003e-0.3349154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"9\" style=\"width: 166px;\"\u003e\n \u003cp\u003e0.04276359\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003eAge (\u0026gt;88 years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 187px;\"\u003e\n \u003cp\u003e-0.8337778\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003eEarly appropriate weight-bearing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 187px;\"\u003e\n \u003cp\u003e0.6735787\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003eAO/OTA typing (Type C)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 187px;\"\u003e\n \u003cp\u003e-0.5708824\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003eBMI (\u0026gt;28 kg/m\u0026sup2;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 187px;\"\u003e\n \u003cp\u003e-0.8187149\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003eSex (Female)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 187px;\"\u003e\n \u003cp\u003e-0.0561418\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003eDiabetes mellitus (DM)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 187px;\"\u003e\n \u003cp\u003e-0.2732441\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003eOsteoporosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 187px;\"\u003e\n \u003cp\u003e-1.9110700\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003eType of\u0026nbsp;surgery\u0026nbsp;(Open reduction)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 187px;\"\u003e\n \u003cp\u003e-1.1764856\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 3. Results of multi-factor analysis of poor postoperative recovery after femoral shaft fracture\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"580\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eS.E,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003eWals\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003eSig.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eExp (B)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e35.970\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e19-28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-20.461\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e9191.356\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.998\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e29-38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-2.751\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.815\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e11.400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e0.013-0.315\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e39-48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-1.732\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.639\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e7.343\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.007*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e0.051-0.619\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e49-58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-1.943\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.674\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e8.325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.004*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e0.038-0.536\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e59-68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-0.824\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.575\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e2.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.439\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e0.142-1.352\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e69-78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-0.164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.539\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.093\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.761\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.849\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e0.295-2.439\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e79-88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.356\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.563\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.527\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e1.427\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e0.474-4.300\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003eEarly appropriate weight-bearing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.711\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.298\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e5.689\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.017*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.491\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e0.274-0.881\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003eAO/OTA typing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e23.857\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003eType B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.392\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e1.384\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e1.480\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e0.770-2.844\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003eType C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e1.479\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.329\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e20.239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.000*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e4.387\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e2.304-8.356\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e34.425\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e18.5\u0026ndash;23.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-1.618\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.748\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e4.674\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.031*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e0.046-0.860\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e24\u0026ndash;27.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-1.748\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.777\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e5.060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.024*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e0.038-0.799\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e\u0026ge; 28.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.355\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.793\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.654\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e1.426\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e0.301-6.751\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003eDiabetes mellitus (DM)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e1.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.331\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e10.118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e2.869\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e1.499-5.491\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003eOsteoporosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e1.682\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.491\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e11.722\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e5.377\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e2.053-14.085\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003eType of\u0026nbsp;surgery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e1.182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.269\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e19.360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e3.261\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e1.926-5.522\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eFemoral shaft fractures represent one of the most common long bone fractures resulting from high-energy trauma, for which accurate prediction of postoperative functional recovery holds significant clinical value in developing individualized treatment plans. This study employed LASSO regression for variable selection, successfully establishing and validating a predictive model for postoperative functional outcomes following intramedullary nailing of femoral shaft fractures, identifying critical determinants of recovery. Results demonstrated that age 29\u0026ndash;58 years, BMI 18.5\u0026ndash;27.9 kg/m\u0026sup2;, and early weight-bearing served as protective factors for recovery, while AO/OTA type C fractures, diabetes mellitus, osteoporosis, and open reduction constituted independent risk factors for suboptimal recovery.\u003c/p\u003e\u003cp\u003eThis study identified optimal recovery among patients aged 29\u0026ndash;58 years, a finding closely aligned with recent high-quality research. Saul D \u003cem\u003eet al.\u003c/em\u003e confirmed in their authoritative study that middle-aged populations possess ideal physiological conditions for bone healing, including optimal bone mineral density, adequate vascular supply networks, and active cellular regenerative capacity[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Court-Brown \u003cem\u003eet al.\u003c/em\u003e supported this perspective through large-scale epidemiological research, noting higher fracture union rates in 30-60-year-old patients compared to other age groups[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. This age stratum exhibits advantages including elevated osteocyte activity, appropriately regulated inflammatory responses, favorable vascularization, and fully functional mechanotransduction signaling pathways[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThis study demonstrated significantly superior recovery among patients within the BMI range 18.5\u0026ndash;27.9 kg/m\u0026sup2;, which fully aligns with contemporary evidence-based medical research. Farr JN \u003cem\u003eet al.\u003c/em\u003e elucidated that moderate BMI levels effectively mitigate metabolic disorders and pro-inflammatory states caused by excessive adiposity[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Upadhyay J \u003cem\u003eet al.'s\u003c/em\u003e multicenter prospective study[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] similarly confirmed that normal-to-mildly overweight BMI ranges optimize critical molecular events during bone healing, including enhanced osteoblast proliferation, neovascularization, and matrix mineralization processes[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Furthermore, appropriate body mass index provides essential mechanical loading that activates mechanosensitive ion channels on osteocyte membranes, stimulating Wnt signaling pathway activation and bone remodeling[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eContradicting traditional views, this investigation revealed that physiologically appropriate early weight-bearing actually facilitates postoperative recovery. Sankar \u003cem\u003eet al.'s\u003c/em\u003e prospective RCT[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] definitively demonstrated that properly timed weight-bearing significantly accelerates fracture healing, whereas overly protective non-weight-bearing protocols prolong union time by approximately 20\u0026ndash;30%. Robling \u003cem\u003eet al.'s\u003c/em\u003e mechanistic research[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] comprehensively elucidated the central role of mechanical stimulation in bone healing, proving that moderate mechanical loading activates the essential Wnt/β-catenin signaling pathway to promote osteoblast differentiation and bone matrix formation. Early rational weight-bearing improves fracture-site perfusion and enhances nutrient delivery, thereby accelerating healing[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFractures classified as AO/OTA Type C, representing comminuted fracture patterns, present well-documented challenges in healing extensively investigated in contemporary literature. Lin \u003cem\u003eet al.'s\u003c/em\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] identified multiple fracture fragments and extensive soft tissue compromise as primary determinants impairing union. Santolini \u003cem\u003eet al.'s\u003c/em\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] stratified analysis revealed 3-4-fold higher nonunion rates versus simple fractures, attributable to reduced fracture surface contact, compromised vascularization, and mechanical instability. Massari \u003cem\u003eet al.'s\u003c/em\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] large-scale epidemiological investigation established significant positive correlation between fracture line complexity and prolonged consolidation periods.\u003c/p\u003e\u003cp\u003eThe multiple negative impacts of diabetes on bone healing have been extensively confirmed by numerous high-quality studies. Jiao \u003cem\u003eet al.\u003c/em\u003e[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] found that the bone healing time for diabetic patients is extended by 40\u0026ndash;87%, and the incidence of complications increases by 2-3.4 times. Further research into the mechanisms reveals that chronic hyperglycemia affects fracture healing[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e] by directly inhibiting osteoblast proliferation and differentiation, damaging vascular endothelial cell function, and increasing oxidative stress. Chattopadhyay \u003cem\u003eet al.\u003c/em\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] molecular biology studies found that the levels of key bone morphogenetic proteins (BMP-2, BMP-7) in the serum of diabetic patients are significantly reduced, severely impacting the initiation and maintenance of bone regeneration.\u003c/p\u003e\u003cp\u003eOsteoporosis impairs fracture healing through multifaceted disruption of bone microstructure and cellular functionality. Gorter \u003cem\u003eet al.'s\u003c/em\u003e big-data analysis demonstrated significantly higher healing failure rates among osteoporosis patients versus healthy controls[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Evidence confirms BMD T-score as a potent independent predictor of adverse healing outcomes[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Pathological alterations including trabecular microstructure deterioration, decreased osteocyte density, and impaired bone matrix protein synthesis substantially compromise healing efficiency in osteoporosis patients[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eClosed reduction combined with intramedullary nailing yields superior outcomes versus open reduction in union rates, nonunion incidence, and infection rates. Open reduction necessitates extensive soft-tissue dissection, inevitably disrupting peri-fracture vascular networks and periosteal integrity. The periosteum, a critical structure containing rich osteoprogenitor cells and vascular plexuses, requires preservation; its removal or damage diminishes fracture-site perfusion and significantly impairs osteogenic regenerative capacity[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Chen \u003cem\u003eet al.\u003c/em\u003e report heightened infection risks with open procedures, a major nonunion risk factor [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Korytkowski \u003cem\u003eet al.'s\u003c/em\u003e meta-analytic study confirmed closed reduction's superiority over open reduction across union success rates, healing timeframes, and overall infection incidence[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn summary, this investigation identified seven independent determinants significantly influencing recovery: age, BMI, early appropriate weight-bearing, fracture classification, diabetes mellitus, osteoporosis, and surgical approach. The developed model provides clinicians with quantified individual risk assessment during preoperative or early postoperative phases, supporting formulation of precise therapeutic strategies. By identifying high-risk patients, targeted interventions, such as optimizing glycemic control, intensifying anti-osteoporosis therapy, and implementing personalized rehabilitation, may substantially improve prognostic outlooks and enhance quality of life[\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThis study effectively addressed the multicollinearity issue in high-dimensional data using the LASSO regression method, avoiding the risk of overfitting that can occur with traditional regression analysis. Additionally, the model's robustness was ensured through both internal and external validation. Furthermore, the study included a wide range of predictive variables, such as patient demographics, fracture characteristics, and surgical factors, to construct a comprehensive predictive system. Ultimately, the webpage we designed provides convenience for patients and doctors, enabling personalized postoperative outcome expectations for patients undergoing intramedullary nailing treatment for femoral shaft fractures. However, this study has certain limitations. As a single-center retrospective study, it lacks representativeness, which may introduce selection bias and information bias. Future studies should conduct multi-center prospective validation, including a more diverse patient population, to build a more comprehensive and precise predictive model.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study successfully developed and validated a predictive model for postoperative functional outcomes following intramedullary nailing of femoral shaft fractures. Key determinants include protective factors: age 29-58 years, BMI 18.5-27.9 kg/m\u0026sup2;, and early appropriate weight-bearing; alongside independent risk factors: AO/OTA type C fractures, diabetes mellitus, osteoporosis, and open reduction. The model demonstrates robust discriminative ability and high calibration accuracy, providing a quantitative clinical tool for individualizing treatment strategies and guiding precision rehabilitation. This framework shows significant potential for optimizing patient prognoses and enhancing healthcare service quality in orthopedic trauma management.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eIMN\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eIntramedullary Nailing\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eLASSO\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eLeast Absolute Shrinkage and Selection Operator\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eDM\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eDiabetes Mellitus\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eH-L testing\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eHosmer-Lemeshow testing\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eDCA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eDecision Curve Analysis\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eROC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eReceiver Operating Characteristic\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eBody Mass Index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSE\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eThe Standard Error\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eOR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eThe Odds Ratio\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eOur study obtained approval from the Ethics Committee ofthe Third Hospital of Hebei Medical University, with approval number K2015-002-1. Every human participant has provided their consent.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eAll participants have agreed to the publication of any relevant images in this paper.\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003eThe datasets used or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003eCompeting Interests\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Key Projects of the National Natural Science Foundation of China (Grant No. 32130052), and Natural Science Foundation of Hebei Province (CN) - for Yanzhao Young Scientists Project (Grant No. H2023206519).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eT.Z., G.Z.H., and Q.X. contributed equally to the study. W.C., and H.Z.L. designed the study, supervised the experiments and manuscript. T.Z., G.Z.H., and Q.X. performed the experiments and wrote the manuscript. Y.Z.Z. and Z.Y.H. provided technical support, reviewed the manuscript and made significant revisions. All the authors have read and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank each institution and each researcher who contributed to this article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eCourt-Brown CM, Caesar B. Epidemiology of adult fractures: A review. Injury. 2006 Aug;37(8):691-7.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eBrumback RJ, Jones AL. Interobserver agreement in the classification of open fractures of the tibia. The results of a survey of two hundred and forty-five orthopaedic surgeons. 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Adv Biomed Res. 2014 Jul 31;3:157.\u003c/li\u003e\n \u003cli\u003eChen YH, Liao HJ, Lin SM, Chang CH, Rwei SP, Lan TY. Radiographic outcomes of the treatment of complex femoral shaft fractures (AO/OTA 32-C) with intramedullary nailing: a retrospective analysis of different techniques. J Int Med Res. 2022 Jun;50(6):3000605221103974.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eKorytkowski PD, Panzone JM, Aldahamsheh O, Mubarak Alkhayarin M, Omar Almohamad H, Alhammoud A. Open and closed reduction methods for intramedullary nailing of femoral shaft fractures: A systematic review and meta-analysis of comparative studies. J Clin Orthop Trauma. 2023 Sep 22;44:102256.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eTu JB, Shi C, Zhang Y, et al. Using machine learning techniques to predict the risk of osteoporosis and fractures: A systematic review. Scientific Reports. 2024;14:5114.\u003c/li\u003e\n\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":"bmc-musculoskeletal-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmsd","sideBox":"Learn more about [BMC Musculoskeletal Disorders](http://bmcmusculoskeletdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://author-welcome.nature.com/12891","title":"BMC Musculoskeletal Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Femoral shaft fractures, LASSO, Clinical prediction model, Web-based online nomogram","lastPublishedDoi":"10.21203/rs.3.rs-7080815/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7080815/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eObjective: To investigate factors influencing postoperative functional recovery after intramedullary nailing (IMN) for femoral shaft fractures and to develop a clinical prediction model.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMethods: Patients who underwent IMN for femoral shaft fractures at the Third Hospital of Hebei Medical University between January 1, 2019, and December 31, 2023, were enrolled. Data were retrospectively collected. Patients treated in 2021 formed the validation cohort; patients treated in other years formed the modeling cohort. Least absolute shrinkage and selection operator (LASSO) regression identified factors influencing postoperative functional outcomes. Multivariable logistic regression analysis identified independent predictors, and a predictive model was constructed. Developed an interactive web-based calculator via R Shiny to implement the predictive model for postoperative recovery after intramedullary nailing of femoral shaft fractures.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResults: This study included 672 patients who underwent IMN for femoral shaft fractures. The modeling cohort comprised 545 patients and the validation cohort 127 patients. Overall, 546 patients (81.2%) achieved good functional scores and exhibited satisfactory fracture healing. The mean age was 50.22 ± 17.73 years; males predominated (455 [67.70%]) over females (217 [32.30%]), yielding a male-to-female ratio of 2.10:1. LASSO regression identified significant predictors of postoperative recovery. Multivariable analysis revealed age 29-58 years, BMI 18.5-27.9 kg/m², and early appropriate weight-bearing as protective factors for good recovery. AO/OTA type C fracture, diabetes mellitus (DM), osteoporosis, and open reduction were independent risk factors for adverse outcomes. The model demonstrated strong discriminatory power, with C-indices of 0.895 (95% CI: 0.8654–0.9245) for the training cohort and 0.7848 (95% CI: 0.6947–0.8749) for the validation cohort. Hosmer-Lemeshow (H-L) testing indicated good model calibration. Decision curve analysis (DCA) showed optimal clinical utility when the threshold probability ranged from 0.19 to 1.00. The interactive web calculator developed via R Shiny is accessible at: https://femoralshaftfracture.shinyapps.io/DynNomapp1/.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConclusion: The developed predictive tool provides realistic, personalized postoperative outcome expectations for patients undergoing IMN for femoral shaft fractures. It can also aid clinicians in formulating appropriate surgical plans, ultimately improving patient prognosis.\u003c/p\u003e","manuscriptTitle":"A novel web-based online nomogram was used to predict postoperative function in patients with femoral shaft fractures treated with intramedullary nails","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-30 04:54:52","doi":"10.21203/rs.3.rs-7080815/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-08T12:26:43+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-07T16:10:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"141964015137357070943256711630572807074","date":"2025-09-25T02:11:19+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-13T14:07:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"139212270563084349528617022332363342816","date":"2025-09-03T06:06:32+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-25T10:43:11+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-24T07:36:22+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-18T16:14:33+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-18T01:54:58+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Musculoskeletal Disorders","date":"2025-07-18T01:51:22+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-musculoskeletal-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmsd","sideBox":"Learn more about [BMC Musculoskeletal Disorders](http://bmcmusculoskeletdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://author-welcome.nature.com/12891","title":"BMC Musculoskeletal Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"01060daf-e193-40ff-b850-e5460bd9410e","owner":[],"postedDate":"July 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-11-26T01:38:18+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-30 04:54:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7080815","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7080815","identity":"rs-7080815","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}