Factors predicting functional and radiographic outcomes after arthroscopic partial meniscectomy: A review of the literature
Bibliographic record
Abstract
We reviewed articles examining the associations between factors identifiable before or during arthroscopic partial meniscectomy (APM) and functional and radiographic outcomes. We assessed the magnitude of effect and statistical significance of associations between predictor variables and outcomes within each study. We then qualitatively synthesized these observations across multiple studies. Twenty-five studies met our criteria for inclusion. Eight of the studies had a prospective design. There were no randomized, controlled trials. The studies had important methodological limitations. Greater size of meniscal resection and female gender showed the strongest and most consistent associations with greater radiographic evidence of osteoarthritis across multiple studies. Greater articular cartilage degeneration assessed at surgery, greater size of meniscal resection, greater laxity of the anterior cruciate ligament, and prior surgery on the index knee were the strongest predictors of worse functional outcomes. Patients with worse preoperative health status (SF-36 score <60), Workers' Compensation, and pending litigation had worse functional outcomes, but these factors were examined in only 1 study. There was consistently no significant difference in radiographic or functional outcome between medial or lateral meniscal injury. Clinicians should incorporate these predictors of outcome into their conversations with patients regarding the advantages and drawbacks of APM.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".