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 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 | 0.013 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.039 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".