Measurement properties for muscle strength tests following anterior cruciate ligament and/or meniscus injury: What tests to use and where do we need to go? A systematic review with meta-analyses for the OPTIKNEE consensus
Bibliographic record
Abstract
OBJECTIVES: Critically appraise and summarise the measurement properties of knee muscle strength tests after anterior cruciate ligament (ACL) and/or meniscus injury using the COnsensus-based Standards for the selection of health Measurement INstruments Risk of Bias checklist. DESIGN: Systematic review with meta-analyses. The modified Grading of Recommendations Assessment, Development and Evaluation-guided assessment of evidence quality. DATA SOURCES: Medline, Embase, CINAHL and SPORTSDiscus searched from inception to 5 May 2022. ELIGIBILITY CRITERIA FOR SELECTING STUDIES: Studies evaluating knee extensor or flexor strength test reliability, measurement error, validity, responsiveness or interpretability in individuals with ACL and/or meniscus injuries with a mean injury age of ≤30 years. RESULTS: Thirty-six studies were included involving 31 different muscle strength tests (mode and equipment) in individuals following an ACL injury and/or an isolated meniscus injury. Strength tests were assessed for reliability (n=8), measurement error (n=7), construct validity (n=27) and criterion validity (n=7). Isokinetic concentric extensor and flexor strength tests were the best rated with sufficient intrarater reliability (very low evidence quality) and construct validity (moderate evidence quality). Isotonic extensor and flexor strength tests showed sufficient criterion validity, while isometric extensor strength tests had insufficient construct and criterion validity (high evidence quality). CONCLUSION: Knee extensor and flexor strength tests of individuals with ACL and/or meniscus injury lack evidence supporting their measurement properties. There is an urgent need for high-quality studies on these measurement properties. Until then, isokinetic concentric strength tests are most recommended, with isotonic strength tests a good alternative.
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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 | 0.102 | 0.261 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.023 | 0.038 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".