What tests should be used to assess functional performance in youth and young adults following anterior cruciate ligament or meniscal injury? A systematic review of measurement properties for the OPTIKNEE consensus
Bibliographic record
Abstract
OBJECTIVES: To critically appraise and summarise measurement properties of functional performance tests in individuals following anterior cruciate ligament (ACL) or meniscal injury. DESIGN: Systematic review. DATA SOURCES: Systematic searches were performed in Medline (Ovid), Embase (Ovid), CINAHL (EBSCO) and SPORTSDiscus (EBSCO) on 7 July 2021. ELIGIBILITY CRITERIA FOR SELECTING STUDIES: Studies evaluating at least one measurement property of a functional performance test including individuals following an ACL tear or meniscal injury with a mean injury age of ≤30 years. The COnsensus-based Standards for the selection of health Measurement INstruments Risk of Bias checklist was used to assess methodological quality. A modified Grading of Recommendations Assessment, Development and Evaluation assessed evidence quality. RESULTS: Thirty studies evaluating 26 functional performance tests following ACL injury were included. No studies were found in individuals with an isolated meniscal injury. Included studies evaluated reliability (n=5), measurement error (n=3), construct validity (n=26), structural validity (n=1) and responsiveness (n=1). The Single Leg Hop and Crossover Hop tests showed sufficient intrarater reliability (high and moderate quality evidence, respectively), construct validity (low-quality and moderate-quality evidence, respectively) and responsiveness (low-quality evidence). CONCLUSION: Frequently used functional performance tests for individuals with ACL or meniscal injury lack evidence supporting their measurement properties. The Single Leg Hop and Crossover Hop are currently the most promising tests following ACL injury. High-quality studies are required to facilitate stronger recommendations of performance-based outcomes following ACL or meniscal injury.
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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 | 0.052 | 0.206 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.012 |
| Bibliometrics | 0.015 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| 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".