The MEniscal TeaR Outcome (METRO) study
Bibliographic record
Abstract
Aims: The aim of this study was to describe the baseline characteristics of patients with a meniscal tear and explore associations with 12-month outcomes. Methods: This prospective, national multicentre cohort study recruited patients aged 18 to 55 years presenting to secondary care with MRI-confirmed meniscal tears. All tear types and mechanisms of injury were included. Osteoarthritis severity was assessed using the Whole-Organ Magnetic Resonance Imaging Score (WORMS). Outcomes at 12 months were measured using the Western Ontario Meniscal Evaluation Tool (WOMET) and the Knee Injury and Osteoarthritis Outcome Score (KOOS4). Multivariable regression analysis assessed associations between 17 baseline factors (including age, tear type, mechanical symptoms, arthritis severity, and undergoing surgery) and 12-month outcomes. A total of 201 patients were recruited across eight sites, with a mean age of 43.2 years (SD 9.7). Results: WORMS analysis indicated no or early-stage osteoarthritis. Stepwise logistic regression showed that higher baseline WOMET and KOOS4 scores, as well as undergoing surgery, were significantly associated with improved 12-month outcomes (p < 0.001). Surgery led to a mean 18-point improvement in both WOMET and KOOS4 scores (out of 100). Mechanical symptoms were not significantly associated with outcomes (p > 0.05). Post hoc analysis revealed that patients who underwent surgery were significantly younger (p = 0.011), with no other baseline differences between groups. Conclusion: In patients with MRI-confirmed meniscal tears, surgical intervention and higher baseline outcome scores were associated with significantly better 12-month patient-reported outcomes. Mechanical symptoms did not predict outcome, suggesting that they should not be exclusively used to guide treatment decisions. Further exploration of these findings is required through randomized trials using similar inclusion criteria to this study.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".