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 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 | 0.122 | 0.398 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.014 |
| Bibliometrics | 0.023 | 0.013 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".