The association between chondral lesions and patient-reported outcomes after meniscectomy: Data from the Osteoarthritis Initiative
Bibliographic record
Abstract
Introduction Evidence and guidelines around the treatment of meniscal tears in patients with knee osteoarthritis (OA) are conflicting, and the impact of chondral damage on surgical outcomes is unclear. Objective Our objective was to evaluate patient-reported outcomes (PROs) following meniscectomy and to determine whether the extent of chondral damage is associated with worse outcomes. Methods We utilized data from the Osteoarthritis Initiative and selected participants with radiographic knee OA (Kellgren-Lawrence grades 2 and 3) and magnetic resonance imaging (MRI) available. We identified the MRI before meniscectomy and measured lesion area on sagittal intermediate-weighted fat-suppressed and axial reformatted Double Echo Steady State MRI. The primary analysis included knees with pain in the year preceding arthroscopic surgery (Western Ontario and McMaster Universities Osteoarthritis Index [WOMAC] Pain > 20) and femoral lesion area >2 cm 2 . Secondary analysis included all knees. Results The primary analysis included 66 participants. Average improvements in PROs 2 years postsurgery were 13.2, 7.6, and 12.1 for WOMAC Pain, WOMAC Function, and Knee Injury and Osteoarthritis Outcome Score (KOOS) Pain, respectively. WOMAC Pain improvements were 10.6 (95% confidence interval: 2.3, 18.9) in those with femoral lesion area 2 to 9 cm 2 and 15.1 (95% confidence interval: 8.1, 22.0) in those with femoral lesion area ≥9 cm 2 . Results were consistent in secondary analyses. Conclusions Participants with radiographic knee OA undergoing meniscectomy reported improvements in PROs. Total femoral lesion area varied considerably, but we did not find associations between lesion area and PROs. These preliminary findings suggest that meniscectomy outcomes do not vary by chondral lesion extent in patients with Kellgren-Lawrence grades 2 and 3 OA.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".