Clinical and radiographic outcomes of meniscus surgery and future targets for biologic intervention: A review of data from the MOON Group
Bibliographic record
Abstract
Meniscus injury and treatment occurred with the majority of anterior cruciate ligament reconstructions (ACLR) in the multicenter orthopedic outcomes (MOON) cohort. We describe the patient-reported outcomes, radiographic outcomes, and predictors of pain from meniscus injuries and treatment in the setting of ACLR. Patient-reported outcomes improve significantly following meniscus repair with ACLR, but differences exist based on the meniscus injury laterally (medial or lateral). Patients undergoing medial meniscus repair have worse patient-reported outcomes and more pain compared to those with uninjured menisci. However, lateral meniscal tears can be repaired with similar outcomes as uninjured menisci. Medial meniscal treatment (meniscectomy or repair) results in a significant loss of joint space at 2 years compared to uninjured menisci. Menisci treated with excision had a greater degree of joint space loss compared to those treated with repair. Clinically significant knee pain is more common following injuries to the medial meniscus and increased in patients who undergo early re-operation after initial ACLR. Future research efforts aimed at improving outcomes after combined ACLR and meniscus treatment should focus on optimizing biologic and mechanical environments that promote healing of medial meniscal tears sustained during ACL injury.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".