Computerized Versus Traditional Approaches for Total Knee Arthroplasty: A Quantitative Analysis of Knee Society Score and Western Ontario and <scp>McMaster</scp> Universities Osteoarthritis Index
Bibliographic record
Abstract
Total knee arthroplasty (TKA) is a common surgery for osteoarthritis, with increasing prevalence expected in the near future. This systematic review and meta‐analysis compared the effectiveness of computerized TKA versus traditional TKA, focusing on postoperative outcomes measured by the Western Ontario and McMaster Universities osteoarthritis index (WOMAC) and the Knee Society score (KSS). A search on PubMed and Cochrane databases on November 14, 2023 for retrospective randomized controlled trials (RCTs) yielded data on WOMAC and KSS. The search strategy was predefined, and methodological quality of studies was critically appraised. Two researchers extracted data. Unpaired t‐testing assessed the mean monthly changes in KSS and WOMAC for computer‐aided versus traditional TKA. Review Manager 5.3 was used for data synthesis and analysis. Out of 729 records, five RCTs enrolling 339 patients were eligible and analyzed using a random effects meta‐analysis. The mean monthly ΔKSS score differed significantly between the traditional and computerized groups (11.47 ± 8.76 vs. 9.26 ± 6.05, respectively; p < 0.01). However, the pooled mean difference estimate showed no significant differences (D = 0.20, 95% CI = −0.53 to 0.93, p = 0.59), with high heterogeneity (I2 = 85%, p < 0.001). The mean monthly ΔWOMAC score also differed significantly (−14.18 ± 21.54 vs. −18.43 ± 20.65, respectively; p < 0.05), but again, no significant differences were found in the pooled estimate (D = 0.17, 95% CI = −0.46 to 0.79, p = 0.60), with moderate heterogeneity (I2 = 28%, p = 0.24).There is no significant difference in KSS or WOMAC outcomes between traditional and computerized TKA. The study suggests the need for further research with longer follow‐up periods, more timepoints, and a broader range of patient outcome measures to fully evaluate the advantages and disadvantages of each method.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.046 | 0.091 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.025 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".