Comparison of MAKO Robotic-Assisted and Manual Unicompartmental Knee Arthroplasty: A Meta- Analysis of Radiographic Precision and Short-term Functional Results

Comparison of MAKO Robotic-Assisted and Manual Unicompartmental Knee Arthroplasty: A Meta- Analysis of Radiographic Precision and Short-term Functional Results | 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 Comparison of MAKO Robotic-Assisted and Manual Unicompartmental Knee Arthroplasty: A Meta- Analysis of Radiographic Precision and Short-term Functional Results Changjiao Sun, Xijiu Zhao, Qi Ma, Xiaofei Zhang, Jiawang Lou, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8571619/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Mar, 2026 Read the published version in Journal of Robotic Surgery → Version 1 posted 7 You are reading this latest preprint version Abstract Background This study sought to evaluate whether the MAKO robotic system offers superior radiographic and functional advantages over traditional techniques in unicompartmental knee replacement(UKA) Methods A systematic literature search was performed through October 2025 across multiple electronic platforms, such as PubMed, Web of Science, Cochrane Library, Embase, Scopus, ClinicalTrials.gov, China National Knowledge Infrastructure (CNKI), Wanfang, China Biology Medicine Disc (CBM), and China Science and Technology Journal (CSTD). A total of 8,924 UKAs from 22 studies were included. Results Findings indicated that MAKO-UKA and conventional methods yielded comparable scores for Visual Analogue Scale(VAS, P = 0.46 ), Pain Catastrophizing Scale(PCS, P = 0.3), American Knee Society Score (AKSS, P = 0.63), AKSS Total (P = 0.7), AKSS function(AKSSF, P = 0.66), Oxford Knee Score (OKS, P = 0.59); Forgotten Joint Score (FJS, P = 0.44), Mechanical femorotibial axis (MFTA, P = 0.07), Tibial component Coronal alignment (TCCA, P = 0.45), Tibial component posterior tilt(TCPT, P = 0.64), Lateral femoral component flexion-extension angle (LFCFEA, P = 0.96), Range of motion(ROM, P = 0.29 ), Complication rate(P = 0.53), Periprosthetic joint infection rate(PJI, P = 0.07), Revision rate(P = 0.14). However, MAKO-UKA was associated with improved femoral component coronal alignment (FCCA, P = 0.04), fewer FCCA outliers (P < 0.001), fewer TCCA outliers (P = 0.03), and reduced tibial posterior slope (PTS, P = 0.017), albeit with longer operative time (P < 0.001). Conclusion MAKO-UKA represents a technically advanced, highly accurate surgical option that provides more precise alignment with a favorable safety profile. While early functional outcomes remain comparable to those of C-UKA, the technology’s true impact may emerge over time through improved implant longevity. Continued long-term, high-quality studies evaluating clinical, radiological, and economic outcomes are essential to define the enduring value of MAKO-UKA in contemporary joint arthroplasty. Unicompartmental knee arthroplasty Robotic-assisted Meta-analysis Mako Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction To address long-standing challenges in component placement precision, robotic-assisted platforms for UKA were developed. These systems aim to optimize joint kinematics and potentially extend prosthesis survival by minimizing human error in bone resection and alignment[ 1 ]. However, as with any emerging technology, questions remain regarding its reliability in consistently achieving these goals. Previous meta-analyses comparing robotic-assisted UKA and conventional UKA (C-UKA) have reported heterogeneous findings due to the inclusion of different robotic systems[ 2 – 6 ]. Because each robotic platform differs in technical functionality and prosthesis integration, its clinical performance should be evaluated independently. Among these systems, the Mako SmartRobotics™ platform has gained increasing clinical adoption over the past decade for its ability to enhance the precision of bone preparation and improve component alignment in UKA [ 7 ]. Despite its widespread use, few high-quality meta-analyses have focused specifically on the Mako system. To date, only one meta-analysis has directly compared the radiological and clinical outcomes of Mako-assisted UKA (MAKO-UKA) and C- UKA; however, that study included literature published only up to November 2021 and was restricted to English-language publications [ 8 ]. Consequently, its findings may not adequately represent current surgical practices or reflect data from non-English regions where the use of MAKO-UKA has grown substantially in recent years. Therefore, updated and more comprehensive evidence is warranted to capture contemporary global experience. We therefore performed this quantitative synthesis to consolidate the latest evidence comparing the surgical efficacy and safety profile of the MAKO platform against manual UKA procedures. The aims of this systematic review and meta-analysis were: (i) to evaluate pain outcomes including Visual Analogue Scale (VAS) and Pain Catastrophizing Scale (PCS) following MAKO-UKA compared with C-UKA; (ii) to assess differences in functional outcomes including American Knee Society Score (AKSS), AKSS Total, AKSS function (AKSSF), Oxford Knee Score (OKS), and Forgotten Joint Score (FJS) between MAKO-UKA and C-UKA; (iii) to assess differences in radiological implant positioning and alignment accuracy including Mechanical femorotibial axis (MFTA), Femoral component Coronal alignment (FCCA), Tibial component Coronal alignment (TCCA), Tibial component posterior tilt(TCPT), Lateral femoral component flexion-extension angle (LFCFEA), FCCA outliers, and TCCA outliers between the two techniques; and (iv) to analyze perioperative metrics, including operative time, range of motion(ROM), Complication rate, Periprosthetic joint infection (PJI) rate, and revision rate. Methods Prior to its commencement, the protocol for this systematic review was formally documented in the PROSPERO database (ID: CRD420250632120; available at: https://www.crd.york.ac.uk/PROSPERO/view/CRD420250632120 ), following the PRISMA guidelines to ensure transparency and rigor [ 9 ]. Definitions for radiological outcomes, including MFTA, FCCA, TCCA, TCPT, and LFCFEA, are provided in Table 1 . Search strategy A comprehensive search of the PubMed, Web of Science, Cochrane Library, Embase, Scopus, ClinicalTrials.gov, China National Knowledge Infrastructure (CNKI), Wanfang, China Biology Medicine Disc (CBM), and China Science and Technology Journal (CSTD) databases was performed to identify comparative studies of MAKO-UKA and C-UKA published before October 2025. The detailed search algorithms for each database, including Boolean operators and search terms, are provided in Supplementary Data 1. Two independent reviewers (X.J.Z and Q.M.) screened all titles, abstracts, and full texts to identify eligible studies. Discrepancies were resolved through consultation with a third senior investigator (C.J.S). Inter-rater reliability was evaluated using Cohen’s κ coefficient. Inclusion and exclusion criteria Studies qualified for inclusion if they utilized a comparative design (such as RCTs or observational cohorts) focusing on patients with end-stage knee osteoarthritis treated with either robotic or manual UKA. Eligibility further required the assessment of at least one clinical or radiographic parameter, including VAS, PCS, AKSS, AKSS Total, AKSSF, OKS, FJS, MFTA, FCCA, TCCA, TCPT, LFCFEA, FCCA outlier, TCCA outlier, operation time, ROM, Complication rate, PJI rate, and revision rate. Furthermore, we only considered studies providing enough raw data to derive effect sizes, specifically risk ratios (RR), odds ratios (OR), or mean differences (MD). Conversely, non-primary research—such as narrative reviews, editorials, and case series—along with conference abstracts and any publications lacking extractable quantitative results, were strictly omitted from this analysis. Data extraction process A pair of independent investigators (X.J.Z and Q.M.) performed the data harvesting. In instances of disagreement, a senior author (C.J.S.)was consulted to reach a consensus. The information harvested from each trial spanned three primary domains: trial-specific descriptors (including authorship, publication year, geographic origin, and methodology), patient-level baseline attributes (notably age, gender distribution, body mass index, and monitoring duration), and all pre-specified clinical outcomes. To maintain the integrity of our dataset, we proactively reached out to the primary investigators via electronic correspondence whenever essential data points were absent or required further explanation. Data Transformation For trials that reported medians with ranges or interquartile ranges, we reconstructed the corresponding means and standard deviations (SDs) to ensure data consistency. This approximation was based on the mathematical frameworks validated by Luo et al.[ 9 ] and Wan et al. [ 10 ]. Such imputation techniques are well documented in the literature [ 11 – 14 ]. Such imputation techniques are well documented in the literature Quality Assessment The methodological rigor of the included evidence was assessed using specialized instruments tailored to the study design. Specifically, the Newcastle–Ottawa Scale (NOS)[ 13 ] was applied to observational cohorts, with a particular emphasis on the integrity of patient selection, the adequacy of comparability controls, and the reliability of outcome tracking. Concurrently, the risk of bias in randomized controlled trials (RCTs) was assessed using the standard frameworks documented in the Cochrane Handbook. This critical appraisal was conducted in parallel by two researchers (X.J.Z. and Q.M.). Any divergent assessments of study quality were reconciled through consultation with a senior investigator (C.J.S.) until consensus was reached. Data Analyses Statistical computations were performed using Stata (version 18.0; StataCorp). Continuous data were presented as mean ± SD. We quantified inter-study inconsistency using the \(\:{I}^{2}\) index, while the Cochran Q test was employed to identify significant heterogeneity. Given anticipated clinical and methodological diversity, all analyses employed a random-effects model using the Restricted Maximum Likelihood (REML) method [ 17 ]. ໿ Effect sizes were expressed with 95% confidence intervals (CIs). Dichotomous variables (e.g., FCCA outliers and TCCA outliers) were analysed using odds ratios (ORs), which approximate relative risk (RR) under Cornfield’s rare-outcome assumption [ 18 ]. Continuous outcomes (VAS, PCS, AKSS, AKSS Total, AKSSF, OKS, FJS, MFTA, FCCA, TCCA, TCPT, LFCFEA, operative time, and ROM) were assessed using mean differences (MDs). Statistical significance was defined by a two-tailed P-value threshold of 0.05. To detect potential publication bias, we applied Egger’s linear regression test to any outcome synthesized from eight or more independent datasets, with a P-value below 0.10 serving as the diagnostic cutoff for significant asymmetry. Furthermore, the stability of our meta-analytic results was verified through leave-one-out sensitivity procedures; by iteratively excluding one study at a time, we ensured that the overall pooled estimates remained resilient and were not disproportionately influenced by any single trial. Search results The PRISMA flow diagram summarizes the study selection process. (Fig. 1 ) The complete search strategy used for each database is provided in Supplementary Data 1. The initial database search yielded 678 records. After importing these records into Zotero, 164 duplicate records were removed. Of the remaining 514 unique articles, 487 were excluded after review of titles and abstracts for irrelevant populations, interventions, study types, or outcomes. The remaining 27 full-text articles were assessed, and 5 were excluded for insufficient or inconsistent outcome data. Ultimately, 22 studies met the inclusion criteria. Inter-reviewer agreement was excellent (κ = 0.9040 for title/abstract screening; κ = 0.8670 for full-text review) (see Supplement data 2). The progression of our literature identification and selection is illustrated in the PRISMA flowchart (Fig. 1 ), with detailed search protocols for each electronic registry documented in Supplementary Data 1. From an initial pool of 678 citations, we eliminated 164 duplicates using Zotero. This left 514 unique records for the preliminary screening phase. A rigorous evaluation of titles and abstracts led to the exclusion of 487 papers deemed irrelevant due to their population, intervention, or study design. Subsequently, 27 potentially eligible articles underwent a comprehensive full-text appraisal; of these, 5 were discarded due to methodological flaws or incomplete data reporting. Consequently, 22 high-quality studies were included. The reliability of this selection process was confirmed by high inter-rater consistency (Cohen’s κ = 0.9040 for initial screening and κ = 0.8670 for the full-text stage; see Supplement 2). Sample Characteristics Tables 2 and 3 provide a detailed overview of the baseline attributes and clinical outcomes collected from each trial. The literature identified for this systematic review was published between 2010 and 2025. Results Quality assessment Fifteen non-randomized studies achieved NOS scores of 7–8 (out of 9), indicating moderate-to-good quality (Table 4 ). Common limitations included incomplete follow-up and potential outcome assessment bias, which are inherent to observational designs. Our quality appraisal of the seven RCTs, conducted via the Cochrane risk-of-bias instrument, is summarized in Table 5 . The methodological integrity of these trials was generally satisfactory, particularly regarding the robust execution of randomization and the concealment of treatment allocation. Nonetheless, performance and detection bias (specifically the blinding of subjects and surgical staff) were consistently rated as high risk; this is an inherent limitation in orthopedic research, where the physical nature of robotic versus manual procedures precludes effective masking. Publication Bias Assessment " In instances where outcomes were supported by at least eight independent datasets, we utilized Egger’s regression to screen for potential publication bias. Our analysis indicated a lack of significant asymmetry for TCCA (P = 0.5403), complication incidence (P = 0.7244), and revision requirements (P = 0.6293), suggesting that the pooled results for these parameters were not substantially influenced by small-study effects. (see Supplementary Data 3). Sensitivity Analysis Leave-one-out analyses demonstrated that pooled estimates remained largely stable, confirming the robustness of the results. However, the PJI rate showed sensitivity to individual study exclusion (see Supplementary Data 4). Outcome measurement Pain Pain administered across these studies included Pain VAS(n = 7) and PCS(n = 2). Statistical analysis revealed that the robotic group did not significantly outperform the manual group in subjective pain relief with Pain VAS (MD = -2.11, 95% CI [-7.68, 3.46], P = 0.46, Fig. 2 .1) and PCS (MD = 0.68, 95% CI [-0.6, 1.96], P = 0.3, Fig. 2 .2). Function outcome results Function scales administered across these studies included AKSS total(n = 3)、AKSS(n = 4)、AKSS function(n = 4)、OKSS(n = 4) and FJS(n = 3). Regarding functional recovery, our comparative analysis indicated that the robotic cohort did not achieve superior outcomes over the manual group across any of the assessed scales. Statistically equivalent scores were observed for AKSS total (MD = -1.42; 95% CI, -8.54 to 5.7; P = 0.7; Fig. 3.1), individual AKSS (MD = 1.72; 95% CI, -5.29 to 8.73; P = 0.63; Fig. 3.2), and AKSS function (MD = 1.53; 95% CI, -5.36 to 8.41; P = 0.66; Fig. 3.3). Similarly, patient-reported functionality showed no meaningful disparities between the two techniques, as evidenced by the pooled results for OKS (MD = 0.28; 95% CI, -0.74 to 1.3; P = 0.59; Fig. 3.4) and FJS (MD = 3.17; 95% CI, -4.8 to 11.14; P = 0.44; Fig. 3.5). Radiological Implant Position and Alignment Radiological Implant Position and Alignment results administered across these studies included MFTA (n = 3)、FCCA (n = 3)、TCCA (n = 8)、TCPT (n = 7)、LFCFEA(n = 2)、FCCA outlier (n = 2)、TCCA outlier (n = 4). Radiographic comparisons indicated that MAKO-UKA and C-UKA yielded comparable results across several parameters, with no statistically significant disparities observed for MFTA (MD = -0.64; 95% CI, -1.33 to 0.05; P = 0.07; Fig. 4.1), TCCA (MD = -0.45; 95% CI, -1.62 to 0.72; P = 0.45; Fig. 4.3), TCPT (MD = -0.35; 95% CI, -1.83 to 1.13; P = 0.64; Fig. 4.4), or LFCFEA (MD = 0.07; 95% CI, -2.87 to 3.01; P = 0.96; Fig. 4.5). However, robotic assistance demonstrated a advantage in optimizing alignment precision. Specifically, the MAKO-UKA cohort achieved a significantly lower FCCA (MD = -1.17; 95% CI, -2.26 to -0.07; P = 0.04; Fig. 4.2). Furthermore, the use of the robotic platform was associated with a marked improvement in surgical consistency, as evidenced by a substantial reduction in the risk of outliers for both FCCA (OR = 0.21; 95% CI, 0.096 to 0.460; P < 0.001; Fig. 4.6) and TCCA (OR = 0.387; 95% CI, 0.166 to 0.901; P = 0.03; Fig. 4.7) compared to the manual group. Perioperative Metrics results In terms of perioperative performance and safety, MAKO-UKA and C-UKA exhibited comparable results across several key indicators. No statistically significant disparities were observed in range of motion (ROM) (MD = 2.54; 95% CI, -2.13 to 7.2; P = 0.29; Fig. 5.1) or overall safety profiles. Specifically, the two techniques yielded similar risks for general complications (RR = 0.718; 95% CI, 0.258 to 2.004; P = 0.53; Fig. 5.3), prosthetic joint infection (PJI) (RR = 2.1; 95% CI, 0.95 to 4.57; P = 0.07; Fig. 5.4), and surgical revision (RR = 0.67; 95% CI, 0.39 to 1.14; P = 0.14; Fig. 5.5). However, robotic assistance was associated with a significant increase in procedural duration; the pooled analysis indicated that the MAKO-UKA group required an average of 19.69 minutes longer to complete than the manual cohort (MD = 19.69; 95% CI, 11.75 to 27.63; P < 0.001; Fig. 5.2). Discussion By isolating a single robotic platform, this meta-analysis minimizes heterogeneity arising from differing robotic technologies [ 19 – 23 ] and provides a more precise assessment of MAKO-UKA compared with C-UKA. The principal finding of this analysis is that MAKO-UKA achieves significantly improved radiographic alignment and fewer component outliers, yet these advantages do not appear to translate into superior short- to mid-term clinical outcomes. This discrepancy highlights the ongoing debate over whether enhanced mechanical precision necessarily yields improved functional outcomes [ 25 , 26 ]. The MAKO system’s CT-based preoperative planning and intraoperative haptic feedback clearly improve the accuracy and reproducibility of bone resections and implant positioning[ 26 ]. However, patient-reported outcomes are multifactorial and influenced not only by alignment but also by soft-tissue balance, rehabilitation quality, and patient expectation [ 27 ]. As such, radiological precision alone may not be sufficient to yield noticeable clinical benefit in the early postoperative period. Consequently, technical excellence in radiographic accuracy does not immediately translate into superior functional performance in the early stages of recovery. Extended longitudinal monitoring is therefore essential to ascertain whether these alignment improvements ultimately bolster prosthesis longevity or mitigate the risk of late-stage complications From a clinical perspective, the improved alignment achieved with MAKO-UKA is not trivial. Biomechanical evidence underscores that even a 3° positioning error can escalate strain at the tibial baseplate-cement junction by 40%[ 28 ], potentially predisposing to early loosening. Therefore, robotic assistance may enhance implant durability and long-term stability. Nevertheless, the absence of a short-term functional benefit in this analysis suggests that early clinical metrics may not fully capture the potential long-term advantages of improved alignment. A consistent limitation of robotic-assisted procedures remains the longer operative time compared with conventional UKA [ 29 – 31 ]. This finding was reaffirmed in the present analysis. The additional time reflects both the technical setup—such as registration, calibration, and intraoperative verification—and the inherent learning curve associated with robotic workflows. While this initially prolongs surgery, multiple reports have demonstrated that operative efficiency improves substantially with increasing surgeon experience [ 32 ]. As robotic technology continues to evolve with faster registration protocols and improved software interfaces, this time differential may become less clinically significant. Nonetheless, medical centers adopting MAKO systems should anticipate a structured learning period. Both techniques demonstrated comparable safety profiles, with no statistical disparity in overall complications or PJI incidences. While robotic-assisted techniques theoretically reduce iatrogenic soft-tissue trauma and thermal necrosis due to more controlled bone preparation[ 37 ] [ 36 ], these advantages may be counterbalanced by the additional instrumentation and prolonged exposure associated with robotic workflows The sensitivity of PJI outcomes to individual studies indicates that more robust prospective data are required to clarify this relationship. Importantly, the comparable complication rates in this study support the overall safety of MAKO-UKA when performed in appropriate settings[ 33 ]. The economic implications of MAKO-UKA merit careful consideration. The Mako platform entails substantial capital investment, software licensing, maintenance, per-case consumable costs, and additional preoperative imaging [ 34 ]. Current economic analyses suggest that cost-effectiveness is highly volume-dependent. Busy surgical centers may neutralize procedural costs by optimizing clinical throughput and decreasing the long-term burdens of reoperation or adverse events [40,41]. However, smaller centers may find it challenging to achieve financial sustainability unless clear long-term clinical benefits are demonstrated[ 37 ]. In this meta-analysis, no reduction in revision or complication rates was observed, underscoring the continued uncertainty regarding the cost-benefit balance. A comprehensive evaluation of long-term outcomes, including survivorship and cost-utility modeling across healthcare systems, will be essential for defining the true value of MAKO-UKA. In summary, the evidence derived from this pooled analysis demonstrates that MAKO-UKA enhances surgical precision and radiographic accuracy without demonstrating short-term clinical superiority over C-UKA. These findings underscore the distinction between technical excellence and clinical efficacy. The potential long-term benefits of MAKO-UKA precision, including improved implant longevity and reduced complications, remain plausible but unproven. As robotic technology becomes more accessible, the balance between precision, efficiency, and economic feasibility will determine its ultimate role in modern arthroplasty practice. Potential Limitations Certain inherent constraints warrant consideration when weighing the conclusions of this synthesis.Despite an extensive literature search, the number of eligible studies was relatively small for certain endpoints, precluding meta-regression analysis and limiting exploration of sources of heterogeneity. Methodological differences in patient selection, surgical technique, and outcome assessment likely contributed to variability among studies. Moreover, the brevity of observation in most incorporated trials hampers a comprehensive appraisal of multi-year prosthesis survival and sustained functional outcomes. Surgeon-related factors, such as robotic experience and case volume, were also inconsistently reported, which may significantly influence both accuracy and outcomes. Future studies employing standardized methodologies, longer follow-up periods, and subgroup analyses that account for surgeon experience are warranted. Conclusion In conclusion, MAKO-UKA represents a technically advanced, highly accurate surgical option that provides more precise alignment with a favorable safety profile. While early functional outcomes remain comparable to those of C-UKA, the technology’s true impact may emerge over time through improved implant longevity. Continued long-term, high-quality studies evaluating clinical, radiological, and economic outcomes are essential to define the enduring value of MAKO-UKA in modern joint arthroplasty. Declarations Ethics approval and consent to participate Since this research entails a secondary analysis of previously disseminated data without direct human interaction, formal ethical clearance was deemed unnecessary. Competing interests All authors confirm the absence of any relevant competing interests. Funding None. Data availability statement The analytical datasets and harvesting templates underpinning these results are obtainable from the lead author via a justified inquiry. CRediT authorship contribution statement Changjiao Sun: Conceptualization, Writing — original draft, Visualization, Validation, Resources, Methodology, Investigation, Data curation, Formal analysis, Conceptualization, Writing — review & editing. Xijiu Zhao: Investigation, Data curation. Qi Ma: Investigation, Data curation. Xiaofei Zhang: Formal analysis, Software. Jiawang Lou: Resources,Project administration. Xu Cai: Supervision. 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J Orthop Surg Res 20:619. https://doi.org/10.1186/s13018-025-06013-6 Perazzini P, Sembenini P, Alberton F et al (2025) Robotic-assisted partial knee surgery performances: A 10-year follow-up retrospective study. Knee Surg Sports Traumatol Arthrosc 33:2197–2203. https://doi.org/10.1002/ksa.12599 Lonner JH, Kerr GJ (2019) Low rate of iatrogenic complications during unicompartmental knee arthroplasty with two semiautonomous robotic systems. Knee 26:745–749. https://doi.org/10.1016/j.knee.2019.02.005 Alexander K, Karunaratne S, Sidhu V et al (2024) Evaluating the cost of robotic-assisted total and unicompartmental knee arthroplasty. J Robot Surg 18:206. https://doi.org/10.1007/s11701-024-01932-8 Yasen Z, Woffenden H, Robinson AP (2023) Robotic-Assisted Knee Arthroplasty: Insights and Implications From Current Literature. Cureus 15:e50852. https://doi.org/10.7759/cureus.50852 Ruangsomboon P, Ruangsomboon O, Isaranuwatchai W et al (2025) Cost-effectiveness of robotic-assisted versus conventional total knee arthroplasty: an analysis from a middle income country. Acta Orthop 96:716–725. https://doi.org/10.2340/17453674.2025.44753 Gordon AM, Nian P, Mont MA, Golub I (2025) Comparison of implant complications, lengths of stay, and costs among patients undergoing robotic-assisted versus conventional unicompartmental knee arthroplasty. Knee 57:471–476. https://doi.org/10.1016/j.knee.2025.10.004 Tables Tables 1 to 5 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table1Operationaldefinitionsofradiographicmetrics.docx Table2Baselineprofilesanddescriptiveattributesofincludedtrials.docx Table3Comprehensivedatareportingforallsynthesizedclinicalandradiographicendpoints..docx Table4Qualityappraisalscoresforthenonrandomizedevidence.docx Table5Riskofbiasprofilingforrandomizedcontrolledtrials.docx SupplementaryAppendix.zip Cite Share Download PDF Status: Published Journal Publication published 02 Mar, 2026 Read the published version in Journal of Robotic Surgery → Version 1 posted Editorial decision: Revision requested 08 Feb, 2026 Reviews received at journal 08 Feb, 2026 Reviewers agreed at journal 26 Jan, 2026 Reviewers invited by journal 21 Jan, 2026 Editor assigned by journal 13 Jan, 2026 Submission checks completed at journal 13 Jan, 2026 First submitted to journal 11 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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09:26:59","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":19612,"visible":true,"origin":"","legend":"","description":"","filename":"Table1Operationaldefinitionsofradiographicmetrics.docx","url":"https://assets-eu.researchsquare.com/files/rs-8571619/v1/7b122bf74a0f9ce9ce4ced8e.docx"},{"id":101170416,"identity":"7829da74-68e3-46a8-a466-3e0a0fe280a1","added_by":"auto","created_at":"2026-01-27 00:02:52","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":29326,"visible":true,"origin":"","legend":"","description":"","filename":"Table2Baselineprofilesanddescriptiveattributesofincludedtrials.docx","url":"https://assets-eu.researchsquare.com/files/rs-8571619/v1/d3289a2281ca3260c02f4e5a.docx"},{"id":101170406,"identity":"6fa1fe5f-c68a-45e2-a975-2f0e37334f82","added_by":"auto","created_at":"2026-01-27 00:02:52","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":25361,"visible":true,"origin":"","legend":"","description":"","filename":"Table3Comprehensivedatareportingforallsynthesizedclinicalandradiographicendpoints..docx","url":"https://assets-eu.researchsquare.com/files/rs-8571619/v1/ee233edee2d3cd0d55134db7.docx"},{"id":101206491,"identity":"2ee66432-7e3a-4b3c-8898-ab9486894360","added_by":"auto","created_at":"2026-01-27 09:56:23","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":21498,"visible":true,"origin":"","legend":"","description":"","filename":"Table4Qualityappraisalscoresforthenonrandomizedevidence.docx","url":"https://assets-eu.researchsquare.com/files/rs-8571619/v1/6bd0e61e00343759c0d2c277.docx"},{"id":101170411,"identity":"36907349-b2cf-4a29-b752-239aeaf4fb44","added_by":"auto","created_at":"2026-01-27 00:02:52","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":19361,"visible":true,"origin":"","legend":"","description":"","filename":"Table5Riskofbiasprofilingforrandomizedcontrolledtrials.docx","url":"https://assets-eu.researchsquare.com/files/rs-8571619/v1/54dd154e561a79e202b52c96.docx"},{"id":101170420,"identity":"22ddbabe-1e03-483d-9907-c417dbc8a513","added_by":"auto","created_at":"2026-01-27 00:02:52","extension":"zip","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":1370613,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryAppendix.zip","url":"https://assets-eu.researchsquare.com/files/rs-8571619/v1/ae774044bb8849d9336c6d4d.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comparison of MAKO Robotic-Assisted and Manual Unicompartmental Knee Arthroplasty: A Meta- Analysis of Radiographic Precision and Short-term Functional Results","fulltext":[{"header":"Introduction","content":"\u003cp\u003eTo address long-standing challenges in component placement precision, robotic-assisted platforms for UKA were developed. These systems aim to optimize joint kinematics and potentially extend prosthesis survival by minimizing human error in bone resection and alignment[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. However, as with any emerging technology, questions remain regarding its reliability in consistently achieving these goals. Previous meta-analyses comparing robotic-assisted UKA and conventional UKA (C-UKA) have reported heterogeneous findings due to the inclusion of different robotic systems[\u003cspan additionalcitationids=\"CR3 CR4 CR5\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Because each robotic platform differs in technical functionality and prosthesis integration, its clinical performance should be evaluated independently.\u003c/p\u003e \u003cp\u003eAmong these systems, the Mako SmartRobotics\u0026trade; platform has gained increasing clinical adoption over the past decade for its ability to enhance the precision of bone preparation and improve component alignment in UKA [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Despite its widespread use, few high-quality meta-analyses have focused specifically on the Mako system. To date, only one meta-analysis has directly compared the radiological and clinical outcomes of Mako-assisted UKA (MAKO-UKA) and C- UKA; however, that study included literature published only up to November 2021 and was restricted to English-language publications [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Consequently, its findings may not adequately represent current surgical practices or reflect data from non-English regions where the use of MAKO-UKA has grown substantially in recent years. Therefore, updated and more comprehensive evidence is warranted to capture contemporary global experience. We therefore performed this quantitative synthesis to consolidate the latest evidence comparing the surgical efficacy and safety profile of the MAKO platform against manual UKA procedures.\u003c/p\u003e \u003cp\u003eThe aims of this systematic review and meta-analysis were: (i) to evaluate pain outcomes including Visual Analogue Scale (VAS) and Pain Catastrophizing Scale (PCS) following MAKO-UKA compared with C-UKA; (ii) to assess differences in functional outcomes including American Knee Society Score (AKSS), AKSS Total, AKSS function (AKSSF), Oxford Knee Score (OKS), and Forgotten Joint Score (FJS) between MAKO-UKA and C-UKA; (iii) to assess differences in radiological implant positioning and alignment accuracy including Mechanical femorotibial axis (MFTA), Femoral component Coronal alignment (FCCA), Tibial component Coronal alignment (TCCA), Tibial component posterior tilt(TCPT), Lateral femoral component flexion-extension angle (LFCFEA), FCCA outliers, and TCCA outliers between the two techniques; and (iv) to analyze perioperative metrics, including operative time, range of motion(ROM), Complication rate, Periprosthetic joint infection (PJI) rate, and revision rate.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003ePrior to its commencement, the protocol for this systematic review was formally documented in the PROSPERO database (ID: CRD420250632120; available at: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.crd.york.ac.uk/PROSPERO/view/CRD420250632120\u003c/span\u003e\u003cspan address=\"https://www.crd.york.ac.uk/PROSPERO/view/CRD420250632120\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), following the PRISMA guidelines to ensure transparency and rigor [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Definitions for radiological outcomes, including MFTA, FCCA, TCCA, TCPT, and LFCFEA, are provided in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSearch strategy\u003c/h2\u003e \u003cp\u003eA comprehensive search of the PubMed, Web of Science, Cochrane Library, Embase, Scopus, ClinicalTrials.gov, China National Knowledge Infrastructure (CNKI), Wanfang, China Biology Medicine Disc (CBM), and China Science and Technology Journal (CSTD) databases was performed to identify comparative studies of MAKO-UKA and C-UKA published before October 2025. The detailed search algorithms for each database, including Boolean operators and search terms, are provided in Supplementary Data 1.\u003c/p\u003e \u003cp\u003eTwo independent reviewers (X.J.Z and Q.M.) screened all titles, abstracts, and full texts to identify eligible studies. Discrepancies were resolved through consultation with a third senior investigator (C.J.S). Inter-rater reliability was evaluated using Cohen\u0026rsquo;s κ coefficient.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eInclusion and exclusion criteria\u003c/h3\u003e\n\u003cp\u003eStudies qualified for inclusion if they utilized a comparative design (such as RCTs or observational cohorts) focusing on patients with end-stage knee osteoarthritis treated with either robotic or manual UKA. Eligibility further required the assessment of at least one clinical or radiographic parameter, including VAS, PCS, AKSS, AKSS Total, AKSSF, OKS, FJS, MFTA, FCCA, TCCA, TCPT, LFCFEA, FCCA outlier, TCCA outlier, operation time, ROM, Complication rate, PJI rate, and revision rate. Furthermore, we only considered studies providing enough raw data to derive effect sizes, specifically risk ratios (RR), odds ratios (OR), or mean differences (MD). Conversely, non-primary research\u0026mdash;such as narrative reviews, editorials, and case series\u0026mdash;along with conference abstracts and any publications lacking extractable quantitative results, were strictly omitted from this analysis.\u003c/p\u003e\n\u003ch3\u003eData extraction process\u003c/h3\u003e\n\u003cp\u003eA pair of independent investigators (X.J.Z and Q.M.) performed the data harvesting. In instances of disagreement, a senior author (C.J.S.)was consulted to reach a consensus. The information harvested from each trial spanned three primary domains: trial-specific descriptors (including authorship, publication year, geographic origin, and methodology), patient-level baseline attributes (notably age, gender distribution, body mass index, and monitoring duration), and all pre-specified clinical outcomes. To maintain the integrity of our dataset, we proactively reached out to the primary investigators via electronic correspondence whenever essential data points were absent or required further explanation.\u003c/p\u003e\n\u003ch3\u003eData Transformation\u003c/h3\u003e\n\u003cp\u003eFor trials that reported medians with ranges or interquartile ranges, we reconstructed the corresponding means and standard deviations (SDs) to ensure data consistency. This approximation was based on the mathematical frameworks validated by Luo et al.[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] and Wan et al. [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Such imputation techniques are well documented in the literature [\u003cspan additionalcitationids=\"CR12 CR13\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Such imputation techniques are well documented in the literature\u003c/p\u003e\n\u003ch3\u003eQuality Assessment\u003c/h3\u003e\n\u003cp\u003eThe methodological rigor of the included evidence was assessed using specialized instruments tailored to the study design. Specifically, the Newcastle\u0026ndash;Ottawa Scale (NOS)[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] was applied to observational cohorts, with a particular emphasis on the integrity of patient selection, the adequacy of comparability controls, and the reliability of outcome tracking. Concurrently, the risk of bias in randomized controlled trials (RCTs) was assessed using the standard frameworks documented in the Cochrane Handbook. This critical appraisal was conducted in parallel by two researchers (X.J.Z. and Q.M.). Any divergent assessments of study quality were reconciled through consultation with a senior investigator (C.J.S.) until consensus was reached.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eData Analyses\u003c/h2\u003e \u003cp\u003eStatistical computations were performed using Stata (version 18.0; StataCorp). Continuous data were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD. We quantified inter-study inconsistency using the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{I}^{2}\\)\u003c/span\u003e\u003c/span\u003e index, while the Cochran Q test was employed to identify significant heterogeneity. Given anticipated clinical and methodological diversity, all analyses employed a random-effects model using the Restricted Maximum Likelihood (REML) method [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e໿ Effect sizes were expressed with 95% confidence intervals (CIs). Dichotomous variables (e.g., FCCA outliers and TCCA outliers) were analysed using odds ratios (ORs), which approximate relative risk (RR) under Cornfield\u0026rsquo;s rare-outcome assumption [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Continuous outcomes (VAS, PCS, AKSS, AKSS Total, AKSSF, OKS, FJS, MFTA, FCCA, TCCA, TCPT, LFCFEA, operative time, and ROM) were assessed using mean differences (MDs). Statistical significance was defined by a two-tailed P-value threshold of 0.05. To detect potential publication bias, we applied Egger\u0026rsquo;s linear regression test to any outcome synthesized from eight or more independent datasets, with a P-value below 0.10 serving as the diagnostic cutoff for significant asymmetry. Furthermore, the stability of our meta-analytic results was verified through leave-one-out sensitivity procedures; by iteratively excluding one study at a time, we ensured that the overall pooled estimates remained resilient and were not disproportionately influenced by any single trial.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSearch results\u003c/h3\u003e\n\u003cp\u003eThe PRISMA flow diagram summarizes the study selection process. (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003e) The complete search strategy used for each database is provided in Supplementary Data 1. The initial database search yielded 678 records. After importing these records into Zotero, 164 duplicate records were removed. Of the remaining 514 unique articles, 487 were excluded after review of titles and abstracts for irrelevant populations, interventions, study types, or outcomes. The remaining 27 full-text articles were assessed, and 5 were excluded for insufficient or inconsistent outcome data. Ultimately, 22 studies met the inclusion criteria. Inter-reviewer agreement was excellent (κ\u0026thinsp;=\u0026thinsp;0.9040 for title/abstract screening; κ\u0026thinsp;=\u0026thinsp;0.8670 for full-text review) (see Supplement data 2).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe progression of our literature identification and selection is illustrated in the PRISMA flowchart (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003e), with detailed search protocols for each electronic registry documented in Supplementary Data 1. From an initial pool of 678 citations, we eliminated 164 duplicates using Zotero. This left 514 unique records for the preliminary screening phase. A rigorous evaluation of titles and abstracts led to the exclusion of 487 papers deemed irrelevant due to their population, intervention, or study design. Subsequently, 27 potentially eligible articles underwent a comprehensive full-text appraisal; of these, 5 were discarded due to methodological flaws or incomplete data reporting. Consequently, 22 high-quality studies were included. The reliability of this selection process was confirmed by high inter-rater consistency (Cohen\u0026rsquo;s κ\u0026thinsp;=\u0026thinsp;0.9040 for initial screening and κ\u0026thinsp;=\u0026thinsp;0.8670 for the full-text stage; see Supplement 2).\u003c/p\u003e\n\u003ch3\u003eSample Characteristics\u003c/h3\u003e\n\u003cp\u003eTables\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e provide a detailed overview of the baseline attributes and clinical outcomes collected from each trial. The literature identified for this systematic review was published between 2010 and 2025.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eQuality assessment\u003c/h2\u003e \u003cp\u003eFifteen non-randomized studies achieved NOS scores of 7\u0026ndash;8 (out of 9), indicating moderate-to-good quality (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Common limitations included incomplete follow-up and potential outcome assessment bias, which are inherent to observational designs.\u003c/p\u003e \u003cp\u003eOur quality appraisal of the seven RCTs, conducted via the Cochrane risk-of-bias instrument, is summarized in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e5\u003c/span\u003e. The methodological integrity of these trials was generally satisfactory, particularly regarding the robust execution of randomization and the concealment of treatment allocation. Nonetheless, performance and detection bias (specifically the blinding of subjects and surgical staff) were consistently rated as high risk; this is an inherent limitation in orthopedic research, where the physical nature of robotic versus manual procedures precludes effective masking.\u003c/p\u003e \u003cp\u003e \u003cb\u003ePublication Bias Assessment\u003c/b\u003e\"\u003c/p\u003e \u003cp\u003eIn instances where outcomes were supported by at least eight independent datasets, we utilized Egger\u0026rsquo;s regression to screen for potential publication bias. Our analysis indicated a lack of significant asymmetry for TCCA (P\u0026thinsp;=\u0026thinsp;0.5403), complication incidence (P\u0026thinsp;=\u0026thinsp;0.7244), and revision requirements (P\u0026thinsp;=\u0026thinsp;0.6293), suggesting that the pooled results for these parameters were not substantially influenced by small-study effects. (see Supplementary Data 3).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eSensitivity Analysis\u003c/h2\u003e \u003cp\u003eLeave-one-out analyses demonstrated that pooled estimates remained largely stable, confirming the robustness of the results. However, the PJI rate showed sensitivity to individual study exclusion (see Supplementary Data 4).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eOutcome measurement\u003c/h2\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003ePain\u003c/h2\u003e \u003cp\u003ePain administered across these studies included Pain VAS(n\u0026thinsp;=\u0026thinsp;7) and PCS(n\u0026thinsp;=\u0026thinsp;2). Statistical analysis revealed that the robotic group did not significantly outperform the manual group in subjective pain relief with Pain VAS (MD = -2.11, 95% CI [-7.68, 3.46], P\u0026thinsp;=\u0026thinsp;0.46, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003e .1) and PCS (MD\u0026thinsp;=\u0026thinsp;0.68, 95% CI [-0.6, 1.96], P\u0026thinsp;=\u0026thinsp;0.3, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003e .2).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eFunction outcome results\u003c/h2\u003e \u003cp\u003eFunction scales administered across these studies included AKSS total(n\u0026thinsp;=\u0026thinsp;3)、AKSS(n\u0026thinsp;=\u0026thinsp;4)、AKSS function(n\u0026thinsp;=\u0026thinsp;4)、OKSS(n\u0026thinsp;=\u0026thinsp;4) and FJS(n\u0026thinsp;=\u0026thinsp;3). Regarding functional recovery, our comparative analysis indicated that the robotic cohort did not achieve superior outcomes over the manual group across any of the assessed scales. Statistically equivalent scores were observed for AKSS total (MD = -1.42; 95% CI, -8.54 to 5.7; P\u0026thinsp;=\u0026thinsp;0.7; Fig.\u0026nbsp;3.1), individual AKSS (MD\u0026thinsp;=\u0026thinsp;1.72; 95% CI, -5.29 to 8.73; P\u0026thinsp;=\u0026thinsp;0.63; Fig.\u0026nbsp;3.2), and AKSS function (MD\u0026thinsp;=\u0026thinsp;1.53; 95% CI, -5.36 to 8.41; P\u0026thinsp;=\u0026thinsp;0.66; Fig.\u0026nbsp;3.3). Similarly, patient-reported functionality showed no meaningful disparities between the two techniques, as evidenced by the pooled results for OKS (MD\u0026thinsp;=\u0026thinsp;0.28; 95% CI, -0.74 to 1.3; P\u0026thinsp;=\u0026thinsp;0.59; Fig.\u0026nbsp;3.4) and FJS (MD\u0026thinsp;=\u0026thinsp;3.17; 95% CI, -4.8 to 11.14; P\u0026thinsp;=\u0026thinsp;0.44; Fig.\u0026nbsp;3.5).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eRadiological Implant Position and Alignment\u003c/h2\u003e \u003cp\u003eRadiological Implant Position and Alignment results administered across these studies included MFTA (n\u0026thinsp;=\u0026thinsp;3)、FCCA (n\u0026thinsp;=\u0026thinsp;3)、TCCA (n\u0026thinsp;=\u0026thinsp;8)、TCPT (n\u0026thinsp;=\u0026thinsp;7)、LFCFEA(n\u0026thinsp;=\u0026thinsp;2)、FCCA outlier (n\u0026thinsp;=\u0026thinsp;2)、TCCA outlier (n\u0026thinsp;=\u0026thinsp;4). Radiographic comparisons indicated that MAKO-UKA and C-UKA yielded comparable results across several parameters, with no statistically significant disparities observed for MFTA (MD = -0.64; 95% CI, -1.33 to 0.05; P\u0026thinsp;=\u0026thinsp;0.07; Fig.\u0026nbsp;4.1), TCCA (MD = -0.45; 95% CI, -1.62 to 0.72; P\u0026thinsp;=\u0026thinsp;0.45; Fig.\u0026nbsp;4.3), TCPT (MD = -0.35; 95% CI, -1.83 to 1.13; P\u0026thinsp;=\u0026thinsp;0.64; Fig.\u0026nbsp;4.4), or LFCFEA (MD\u0026thinsp;=\u0026thinsp;0.07; 95% CI, -2.87 to 3.01; P\u0026thinsp;=\u0026thinsp;0.96; Fig.\u0026nbsp;4.5). However, robotic assistance demonstrated a advantage in optimizing alignment precision. Specifically, the MAKO-UKA cohort achieved a significantly lower FCCA (MD = -1.17; 95% CI, -2.26 to -0.07; P\u0026thinsp;=\u0026thinsp;0.04; Fig.\u0026nbsp;4.2). Furthermore, the use of the robotic platform was associated with a marked improvement in surgical consistency, as evidenced by a substantial reduction in the risk of outliers for both FCCA (OR\u0026thinsp;=\u0026thinsp;0.21; 95% CI, 0.096 to 0.460; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;4.6) and TCCA (OR\u0026thinsp;=\u0026thinsp;0.387; 95% CI, 0.166 to 0.901; P\u0026thinsp;=\u0026thinsp;0.03; Fig.\u0026nbsp;4.7) compared to the manual group.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003ePerioperative Metrics results\u003c/h2\u003e \u003cp\u003eIn terms of perioperative performance and safety, MAKO-UKA and C-UKA exhibited comparable results across several key indicators. No statistically significant disparities were observed in range of motion (ROM) (MD\u0026thinsp;=\u0026thinsp;2.54; 95% CI, -2.13 to 7.2; P\u0026thinsp;=\u0026thinsp;0.29; Fig.\u0026nbsp;5.1) or overall safety profiles. Specifically, the two techniques yielded similar risks for general complications (RR\u0026thinsp;=\u0026thinsp;0.718; 95% CI, 0.258 to 2.004; P\u0026thinsp;=\u0026thinsp;0.53; Fig.\u0026nbsp;5.3), prosthetic joint infection (PJI) (RR\u0026thinsp;=\u0026thinsp;2.1; 95% CI, 0.95 to 4.57; P\u0026thinsp;=\u0026thinsp;0.07; Fig.\u0026nbsp;5.4), and surgical revision (RR\u0026thinsp;=\u0026thinsp;0.67; 95% CI, 0.39 to 1.14; P\u0026thinsp;=\u0026thinsp;0.14; Fig.\u0026nbsp;5.5). However, robotic assistance was associated with a significant increase in procedural duration; the pooled analysis indicated that the MAKO-UKA group required an average of 19.69 minutes longer to complete than the manual cohort (MD\u0026thinsp;=\u0026thinsp;19.69; 95% CI, 11.75 to 27.63; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;5.2).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eBy isolating a single robotic platform, this meta-analysis minimizes heterogeneity arising from differing robotic technologies [\u003cspan additionalcitationids=\"CR20 CR21 CR22\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] and provides a more precise assessment of MAKO-UKA compared with C-UKA.\u003c/p\u003e \u003cp\u003eThe principal finding of this analysis is that MAKO-UKA achieves significantly improved radiographic alignment and fewer component outliers, yet these advantages do not appear to translate into superior short- to mid-term clinical outcomes. This discrepancy highlights the ongoing debate over whether enhanced mechanical precision necessarily yields improved functional outcomes [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The MAKO system\u0026rsquo;s CT-based preoperative planning and intraoperative haptic feedback clearly improve the accuracy and reproducibility of bone resections and implant positioning[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. However, patient-reported outcomes are multifactorial and influenced not only by alignment but also by soft-tissue balance, rehabilitation quality, and patient expectation [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. As such, radiological precision alone may not be sufficient to yield noticeable clinical benefit in the early postoperative period. Consequently, technical excellence in radiographic accuracy does not immediately translate into superior functional performance in the early stages of recovery. Extended longitudinal monitoring is therefore essential to ascertain whether these alignment improvements ultimately bolster prosthesis longevity or mitigate the risk of late-stage complications\u003c/p\u003e \u003cp\u003eFrom a clinical perspective, the improved alignment achieved with MAKO-UKA is not trivial. Biomechanical evidence underscores that even a 3\u0026deg; positioning error can escalate strain at the tibial baseplate-cement junction by 40%[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], potentially predisposing to early loosening. Therefore, robotic assistance may enhance implant durability and long-term stability. Nevertheless, the absence of a short-term functional benefit in this analysis suggests that early clinical metrics may not fully capture the potential long-term advantages of improved alignment.\u003c/p\u003e \u003cp\u003eA consistent limitation of robotic-assisted procedures remains the longer operative time compared with conventional UKA [\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. This finding was reaffirmed in the present analysis. The additional time reflects both the technical setup\u0026mdash;such as registration, calibration, and intraoperative verification\u0026mdash;and the inherent learning curve associated with robotic workflows. While this initially prolongs surgery, multiple reports have demonstrated that operative efficiency improves substantially with increasing surgeon experience [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. As robotic technology continues to evolve with faster registration protocols and improved software interfaces, this time differential may become less clinically significant. Nonetheless, medical centers adopting MAKO systems should anticipate a structured learning period.\u003c/p\u003e \u003cp\u003eBoth techniques demonstrated comparable safety profiles, with no statistical disparity in overall complications or PJI incidences. While robotic-assisted techniques theoretically reduce iatrogenic soft-tissue trauma and thermal necrosis due to more controlled bone preparation[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], these advantages may be counterbalanced by the additional instrumentation and prolonged exposure associated with robotic workflows The sensitivity of PJI outcomes to individual studies indicates that more robust prospective data are required to clarify this relationship. Importantly, the comparable complication rates in this study support the overall safety of MAKO-UKA when performed in appropriate settings[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe economic implications of MAKO-UKA merit careful consideration. The Mako platform entails substantial capital investment, software licensing, maintenance, per-case consumable costs, and additional preoperative imaging [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Current economic analyses suggest that cost-effectiveness is highly volume-dependent. Busy surgical centers may neutralize procedural costs by optimizing clinical throughput and decreasing the long-term burdens of reoperation or adverse events [40,41]. However, smaller centers may find it challenging to achieve financial sustainability unless clear long-term clinical benefits are demonstrated[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. In this meta-analysis, no reduction in revision or complication rates was observed, underscoring the continued uncertainty regarding the cost-benefit balance. A comprehensive evaluation of long-term outcomes, including survivorship and cost-utility modeling across healthcare systems, will be essential for defining the true value of MAKO-UKA.\u003c/p\u003e \u003cp\u003eIn summary, the evidence derived from this pooled analysis demonstrates that MAKO-UKA enhances surgical precision and radiographic accuracy without demonstrating short-term clinical superiority over C-UKA. These findings underscore the distinction between technical excellence and clinical efficacy. The potential long-term benefits of MAKO-UKA precision, including improved implant longevity and reduced complications, remain plausible but unproven. As robotic technology becomes more accessible, the balance between precision, efficiency, and economic feasibility will determine its ultimate role in modern arthroplasty practice.\u003c/p\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003ePotential Limitations\u003c/h2\u003e \u003cp\u003eCertain inherent constraints warrant consideration when weighing the conclusions of this synthesis.Despite an extensive literature search, the number of eligible studies was relatively small for certain endpoints, precluding meta-regression analysis and limiting exploration of sources of heterogeneity. Methodological differences in patient selection, surgical technique, and outcome assessment likely contributed to variability among studies. Moreover, the brevity of observation in most incorporated trials hampers a comprehensive appraisal of multi-year prosthesis survival and sustained functional outcomes. Surgeon-related factors, such as robotic experience and case volume, were also inconsistently reported, which may significantly influence both accuracy and outcomes. Future studies employing standardized methodologies, longer follow-up periods, and subgroup analyses that account for surgeon experience are warranted.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, MAKO-UKA represents a technically advanced, highly accurate surgical option that provides more precise alignment with a favorable safety profile. While early functional outcomes remain comparable to those of C-UKA, the technology\u0026rsquo;s true impact may emerge over time through improved implant longevity. Continued long-term, high-quality studies evaluating clinical, radiological, and economic outcomes are essential to define the enduring value of MAKO-UKA in modern joint arthroplasty.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSince this research entails a secondary analysis of previously disseminated data without direct human interaction, formal ethical clearance was deemed unnecessary.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors confirm the absence of any relevant competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe analytical datasets and harvesting templates underpinning these results are obtainable from the lead author via a justified inquiry.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCRediT authorship contribution statement\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eChangjiao Sun:\u0026nbsp;\u003c/strong\u003eConceptualization, Writing — original draft, Visualization, Validation, Resources, Methodology, Investigation, Data curation, Formal analysis, Conceptualization, Writing — review \u0026amp; editing.\u003cstrong\u003eXijiu Zhao:\u0026nbsp;\u003c/strong\u003eInvestigation, Data curation.\u0026nbsp;\u003cstrong\u003eQi Ma:\u0026nbsp;\u003c/strong\u003eInvestigation, Data curation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eXiaofei Zhang:\u003c/strong\u003e Formal analysis, Software.\u0026nbsp;\u003cstrong\u003eJiawang Lou:\u0026nbsp;\u003c/strong\u003eResources,Project administration.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eXu Cai:\u003c/strong\u003eSupervision.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eFu X, She Y, Jin G et al (2024) Comparison of robotic-assisted total knee arthroplasty: an updated systematic review and meta-analysis. 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Arch Orthop Trauma Surg 144:4827\u0026ndash;4838. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00402-024-05569-y\u003c/span\u003e\u003cspan address=\"10.1007/s00402-024-05569-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAre L, De Mauro D, Rovere G et al (2023) Robotic-assisted unicompartimental knee arthroplasty performed with Navio system: a systematic review. 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J Robot Surg 18:206. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11701-024-01932-8\u003c/span\u003e\u003cspan address=\"10.1007/s11701-024-01932-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYasen Z, Woffenden H, Robinson AP (2023) Robotic-Assisted Knee Arthroplasty: Insights and Implications From Current Literature. Cureus 15:e50852. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.7759/cureus.50852\u003c/span\u003e\u003cspan address=\"10.7759/cureus.50852\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRuangsomboon P, Ruangsomboon O, Isaranuwatchai W et al (2025) Cost-effectiveness of robotic-assisted versus conventional total knee arthroplasty: an analysis from a middle income country. Acta Orthop 96:716\u0026ndash;725. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2340/17453674.2025.44753\u003c/span\u003e\u003cspan address=\"10.2340/17453674.2025.44753\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGordon AM, Nian P, Mont MA, Golub I (2025) Comparison of implant complications, lengths of stay, and costs among patients undergoing robotic-assisted versus conventional unicompartmental knee arthroplasty. Knee 57:471\u0026ndash;476. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.knee.2025.10.004\u003c/span\u003e\u003cspan address=\"10.1016/j.knee.2025.10.004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 5 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-robotic-surgery","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jors","sideBox":"Learn more about [Journal of Robotic Surgery](http://link.springer.com/journal/11701)","snPcode":"11701","submissionUrl":"https://submission.nature.com/new-submission/11701/3","title":"Journal of Robotic Surgery","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Unicompartmental knee arthroplasty, Robotic-assisted, Meta-analysis, Mako","lastPublishedDoi":"10.21203/rs.3.rs-8571619/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8571619/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThis study sought to evaluate whether the MAKO robotic system offers superior radiographic and functional advantages over traditional techniques in unicompartmental knee replacement(UKA)\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA systematic literature search was performed through October 2025 across multiple electronic platforms, such as PubMed, Web of Science, Cochrane Library, Embase, Scopus, ClinicalTrials.gov, China National Knowledge Infrastructure (CNKI), Wanfang, China Biology Medicine Disc (CBM), and China Science and Technology Journal (CSTD). A total of 8,924 UKAs from 22 studies were included.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eFindings indicated that MAKO-UKA and conventional methods yielded comparable scores for Visual Analogue Scale(VAS, P\u0026thinsp;=\u0026thinsp;0.46 ), Pain Catastrophizing Scale(PCS, P\u0026thinsp;=\u0026thinsp;0.3), American Knee Society Score (AKSS, P\u0026thinsp;=\u0026thinsp;0.63), AKSS Total (P\u0026thinsp;=\u0026thinsp;0.7), AKSS function(AKSSF, P\u0026thinsp;=\u0026thinsp;0.66), Oxford Knee Score (OKS, P\u0026thinsp;=\u0026thinsp;0.59); Forgotten Joint Score (FJS, P\u0026thinsp;=\u0026thinsp;0.44), Mechanical femorotibial axis (MFTA, P\u0026thinsp;=\u0026thinsp;0.07), Tibial component Coronal alignment (TCCA, P\u0026thinsp;=\u0026thinsp;0.45), Tibial component posterior tilt(TCPT, P\u0026thinsp;=\u0026thinsp;0.64), Lateral femoral component flexion-extension angle (LFCFEA, P\u0026thinsp;=\u0026thinsp;0.96), Range of motion(ROM, P\u0026thinsp;=\u0026thinsp;0.29 ), Complication rate(P\u0026thinsp;=\u0026thinsp;0.53), Periprosthetic joint infection rate(PJI, P\u0026thinsp;=\u0026thinsp;0.07), Revision rate(P\u0026thinsp;=\u0026thinsp;0.14). However, MAKO-UKA was associated with improved femoral component coronal alignment (FCCA, P\u0026thinsp;=\u0026thinsp;0.04), fewer FCCA outliers (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), fewer TCCA outliers (P\u0026thinsp;=\u0026thinsp;0.03), and reduced tibial posterior slope (PTS, P\u0026thinsp;=\u0026thinsp;0.017), albeit with longer operative time (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eMAKO-UKA represents a technically advanced, highly accurate surgical option that provides more precise alignment with a favorable safety profile. While early functional outcomes remain comparable to those of C-UKA, the technology\u0026rsquo;s true impact may emerge over time through improved implant longevity. Continued long-term, high-quality studies evaluating clinical, radiological, and economic outcomes are essential to define the enduring value of MAKO-UKA in contemporary joint arthroplasty.\u003c/p\u003e","manuscriptTitle":"Comparison of MAKO Robotic-Assisted and Manual Unicompartmental Knee Arthroplasty: A Meta- Analysis of Radiographic Precision and Short-term Functional Results","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-27 00:02:47","doi":"10.21203/rs.3.rs-8571619/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-08T13:53:59+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-08T13:37:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"135474209434660089212020767943262898017","date":"2026-01-26T17:33:50+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-21T16:57:27+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-13T17:53:31+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-13T05:48:09+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Robotic Surgery","date":"2026-01-11T05:28:23+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-robotic-surgery","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jors","sideBox":"Learn more about [Journal of Robotic Surgery](http://link.springer.com/journal/11701)","snPcode":"11701","submissionUrl":"https://submission.nature.com/new-submission/11701/3","title":"Journal of Robotic Surgery","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"3b64db8e-94c8-4fab-86ce-63ca4471858e","owner":[],"postedDate":"January 27th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-09T16:03:56+00:00","versionOfRecord":{"articleIdentity":"rs-8571619","link":"https://doi.org/10.1007/s11701-026-03259-y","journal":{"identity":"journal-of-robotic-surgery","isVorOnly":false,"title":"Journal of Robotic Surgery"},"publishedOn":"2026-03-02 15:57:50","publishedOnDateReadable":"March 2nd, 2026"},"versionCreatedAt":"2026-01-27 00:02:47","video":"","vorDoi":"10.1007/s11701-026-03259-y","vorDoiUrl":"https://doi.org/10.1007/s11701-026-03259-y","workflowStages":[]},"version":"v1","identity":"rs-8571619","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8571619","identity":"rs-8571619","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}