WE7.11 Robotic-assisted versus conventional total knee arthroplasty: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Abstract Aim To compare robotic-assisted total knee arthroplasty (TKA) and conventional TKA on both long-term and short-term follow-up. Methods For conducting this study, we searched four electronic databases. The outcomes were pooled as mean difference (MD) or risk ratio (RR), and 95% confidence interval. We used RevMan for performing the analysis. Results We included nine studies. The data showed a significant favoring of robotic-assisted TKA than the conventional one in mechanical alignment, Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), and femoral coronal outliers (MD= -1.10, 95% CI [-1.51, -0.69], p<0.00001), (MD= -1.19, 95% CI [-2.35, -0.03], p=0.04), and (RR= 0.49, 95% CI [0.30, 0.80], p=0.004) respectively. On the other hand, the conventional TKA was better in range of motion-flexion (long-term) than the robotic-assisted one (MD= -3.02, 95% CI [-3.68, -2.37], p<0.00001). However, there were no significant differences between them in knee society score-knee score, knee society score-function score, change in hospital for special surgery, and change in range of motion-extension (MD= -0.36, 95% CI [-2.43, 1.70], p=0.73), (MD= -0.34, 95% CI [-2.36, 1.68], p=0.74), (MD=0.78, 95% CI [-0.84, 2.40], p=0.34), and (MD=0.16, 95% [-1.32, 1.64], p=0.83) respectively. Conclusion Robotic-assisted TKA had better outcomes than conventional TKA regarding mechanical alignment and WOMAC. However, the conventional approach showed a better range of motion-flexion in the long term. More data is needed for the long-term outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.014 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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; both teacher heads agree on what is shown here.
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".