Effect of exercise therapy versus surgery on mechanical symptoms in young patients with a meniscal tear: a secondary analysis of the DREAM trial
Bibliographic record
Abstract
Objective To compare the effect of early surgery versus exercise and education on mechanical symptoms and other patient-reported outcomes in patients aged 18–40 years with a meniscal tear and self-reported mechanical knee symptoms. Methods In a randomised controlled trial, 121 patients aged 18–40 years with a MRI-verified meniscal tear were randomised to surgery or 12-week supervised exercise and education. For this study, 63 patients (33 and 30 patients in the surgery and in the exercise group, respectively) reporting baseline mechanical symptoms were included. The main 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 KOOS4and 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. We did not detect any between-group differences in the secondary outcomes. Conclusion The results from this secondary analysis suggest that early surgery is more effective than exercise and education for relieving self-reported mechanical knee symptoms, but not for improving pain, function and quality of life in young patients with a meniscal tear and mechanical symptoms. Trial registration number NCT02995551 .
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".