1 The effect of exercise therapy and surgery on mechanical symptoms in young patients with a meniscal tear
Bibliographic record
Abstract
Introduction A common treatment strategy to alleviate mechanical symptoms in young patients with meniscal tears is meniscal surgery, however, it is unknown whether this is superior to a non-surgical strategy. Therefore, we aimed to compare meniscal surgery to early exercise therapy and patient education. Materials and Methods In a randomized controlled trial, 121 patients aged 18–40 years with a MRI-verified meniscal tear were randomized to surgery or 12-weeks supervised exercise and education. For this study 63 patients (33 and 30 patients in surgery and exercise groups, respectively) reporting baseline mechanical symptoms were included. Primary outcome was self-reported mechanical symptoms (yes/no) at 3, 6, and 12 months assessed using a single item from the Knee Injury and Osteoarthritis Outcome Score (KOOS). Secondary outcomes were KOOS4 and the 5 KOOS-subscales and the Western Ontario Meniscal Evaluation Tool (WOMET). Results In total, 55/63 patients completed the 12-month follow-up. At 12 months 9/26 (35%) in the surgery group and 20/29 (69%) in the exercise group reported mechanical symptoms. The risk difference and relative risk at any time point was 28.7% (95% CI 8.6 to 48.8) and 1.83 (95% CI, 0.98 to 2.70) of reporting mechanical symptoms in the exercise group compared with the surgery group. No between group differences were found in secondary outcomes. Conclusion Surgery seems to be more effective for relieving self-reported mechanical symptoms, but not for improving pain, function and quality of life in young patients with a meniscal tear and mechanical symptoms compared with a strategy of exercise and education.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".