Meaningful thresholds for patient-reported outcomes following interventions for anterior cruciate ligament tear or traumatic meniscus injury: a systematic review for the OPTIKNEE consensus
Bibliographic record
Abstract
OBJECTIVE: We synthesised and assessed credibility (ie, trustworthiness) of thresholds that define meaningful scores for patient-reported outcome measures (PROMs) following interventions for anterior cruciate ligament (ACL) tear or traumatic meniscus injury. DESIGN: Systematic review, narrative synthesis. DATA SOURCES: We searched five databases, handsearched references of included studies and tracked citations. ELIGIBILITY: Included studies investigated: individuals with ACL tear or meniscus injury; mean age <35 years; and PROM thresholds calculated using any method to define a minimal important change (MIC) or a meaningful post-treatment score (Patient Acceptable Symptom State (PASS) or Treatment Failure). RESULTS: We included 18 studies (15 ACL, 3 meniscus). Three different methods were used to calculate anchor-based MICs across 9 PROMs, PASS thresholds across 4 PROMs and treatment failure for 1 PROM. Credibility was rated 'high' for only one study-an MIC of 18 for the Knee injury and Osteoarthritis Outcome Score Quality-of-life (KOOS-QOL) subscale (using the MID Credibility Assessment Tool). Where multiple thresholds were calculated among 'low' credibility thresholds in ACL studies, MICs converged to within a 10-point range for KOOS-Symptoms (-1.2 to 5.4) and function in daily living (activities of daily living, ADL 0.5-8.1) subscales, and the International Knee Documentation Committee Subjective Knee Form (7.1-16.2). Other PROM thresholds differed up to 30 points. PASS thresholds converged to within a 10-point range in KOOS-ADL for ACL tears (92.3-100), and KOOS-Symptoms (73-78) and KOOS-QOL (53-57) in meniscus injuries. CONCLUSION: Meaningful PROM thresholds were highly susceptible to study heterogeneity. While PROM thresholds can aid interpretability in research and clinical practice, they should be cautiously interpreted.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.010 | 0.006 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".