Chondral lesions at the medial femoral condyle, meniscal degeneration, anterior cruciate ligament insufficiency, and lateral meniscal tears impair the middle-term results after arthroscopic partial meniscectomy
Bibliographic record
Abstract
PURPOSE: The aim of the present study was to analyse which clinical, radiological and arthroscopic findings are able to predict the postoperative outcome after arthroscopic partial meniscectomy. Furthermore, the present study aimed to investigate the postoperative outcome after partial meniscectomy in patients with degenerative meniscal lesions. METHODS: A total of 91 patients with a follow-up period of 34.7 ± 11.4 months after arthroscopic partial meniscectomy were included in this retrospective study. Clinical, radiological, and arthroscopic data were analysed at the time of follow-up. The multivariable linear regression analysis for postoperative outcome, based on the Western Ontario Meniscal Evaluation Tool (WOMET), included age, gender, body mass index, physical activity, presence of cartilage lesions, leg alignment, grade of radiographic osteoarthritis, location of meniscal lesions, meniscal extrusion, meniscal degeneration, presence of an anterior cruciate ligament tears as well as bone marrow lesions. RESULTS: WOMET and WOMAC scores showed a significant improvement of 45.0 ± 48.1 points (CI 34.9-55.1; p ≤ 0.0001) and 75.1 ± 69.3 points (CI 60.6-89.6; p = 0.001) within the follow-up period. Multivariable linear regression analysis showed that poor preoperative WOMET scores (p = 0.001), presence of cartilage lesions at the medial femoral condylus (p = 0.001), meniscal degeneration (p = 0.008), the presence of an anterior cruciate ligament lesion (p = 0.005), and lateral meniscal tears (p = 0.039) were associated with worse postoperative outcomes. Patients with femoral bone marrow lesions had better outcome (p = 0.038). CONCLUSION: Poor preoperative WOMET scores, presence of cartilage lesions at the medial femoral condylus, meniscal degeneration, concomitant anterior cruciate ligament lesions as well as lateral meniscal tears are correlated with worse postoperative outcomes after arthroscopic partial meniscectomy. Patients with femoral bone marrow lesions femoral are more likely to gain benefit from arthroscopic partial meniscectomy in the middle term. Despite justified recent restrictions in indication, arthroscopic partial meniscectomy seems to effectively reduce pain and alleviate symptoms in carefully selected patients with degenerative meniscal tears. LEVEL OF EVIDENCE: III.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".