Neuromuscular risk factors for non-contact knee injury: a systematic review and meta-analysis
Bibliographic record
Abstract
ABSTRACT Objectives Identify neuromuscular risk factors for non-contact knee injury, using a systematic review and meta-analysis, to inform the development of preventive strategies. Methods Medline, Web of Science and SCOPUS were searched from inception until November 2020. Prospective and nested case-control studies that analysed baseline neuromuscular characteristics as potential risk factors for subsequent non-contact knee injuries were included. Two reviewers independently appraised methodological quality using a modified Newcastle–Ottawa Scale. Meta-analysis was performed where appropriate, with standardised mean differences calculated for continuous scaled data. Results Seventeen studies were included, comprising baseline data from 5,584 participants and 415 non-contact knee injuries (heterogeneous incidence = 7.4%). Protocols and outcome measures differed across studies, limiting data pooling. Twenty-one neuromuscular variables were included in the meta-analysis. Three were identified as risk factors. For patellofemoral pain, among military recruits: reduced non-normalised quadriceps strength at 60º/s (SMD −0.66; 95% CI −0.99, −0.32); reduced quadriceps strength at 240º/s (normalised by body mass) (SMD −0.53; CI −0.87, −0.20). For PFP/ACL injury among female military recruits: reduced quadriceps strength at 60º/s (normalised by body mass) (SMD −0.50; CI −0.92, −0.08). Conclusions Quadriceps weakness is a risk factor for PFP among military recruits, and for PFP/ACL injury among female military recruits. However, the effect sizes are small, and the generalisability of these findings is limited. The effectiveness of quadriceps strengthening interventions for preventing PFP and ACL injury merits evaluation in prospective randomised trials.
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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 | 0.016 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.043 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".