Comparing the Sub-Vastus and Medial Parapatellar Approaches in Total Knee Arthroplasty: A Meta-Analysis of Short-Term Outcomes
Bibliographic record
Abstract
Proponents of the Sub-Vastus (SV) approach in primary Total Knee Arthroplasty (TKA) claim superior extensor mechanism function which results in earlier recovery after surgery. We performed a meta-analysis of randomized controlled trials comparing the SV and standard Medial Parapatellar (MPP) approaches in primary TKAs. The study was performed using the Cochrane handbook for systematic reviews guidelines. A total of 28 studies with 2171 patients were included and the mean age of the SV group was 68.2 [standard deviation, ±4.4] and of the MPP group was 68.4 (±3.85). The SV approach resulted in a significant improvement in flexion at day 3 postoperatively [mean difference (MD) = 6°; 95% confidence interval (CI), 0.37-11.71; p < 0.01], but this was not sustainable at 6 weeks, 3 months, and 12 months postoperatively. Similarly, despite a significant reduction in the visual analogue scale score at day 1 postoperatively (MD = -1.19; 95% CI, -1.70 to -0.68; p < 0.01), there was not enough evidence to support its superiority after that. The SV approach led to a reduction in days to straight leg raise (MD = -1.88; 95% CI, -2.45 to -1.31; p < 0.01) and lateral releases [relative risk = 0.49; 95% CI, 0.30-0.82; p < 0.01) with no difference in complication rates ( p = 0.64) but at the expense of a prolonged operation (MD = 13 min; 95% CI, 9.41-16.69; p < 0.01). Our conclusion is that the SV approach provides an alternative to the MPP with some advantages in the first 3 days only after primary TKA surgery.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.048 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".