Prevention of Non-Contact Anterior Cruciate Ligament Injuries among Youth Female Athletes: An Umbrella Review
Bibliographic record
Abstract
Anterior cruciate ligament (ACL) injuries account for a large percentage of knee injuries, disproportionately affecting female athletes. To help health professionals stay current, we performed an umbrella review to evaluate the effectiveness of ACL injury prevention programs in reducing non-contact ACL injury rates, determine the effective components within interventions, and provide clinical recommendations. Twelve databases (Medline, Embase, Cochrane Database of Systematic Reviews, SPORTDiscus, Cumulative Index to Nursing and Allied Health Literature, PEDro, Web of Science Core Collection, Epistemonikos, TRIP, BC Guidelines and Protocols, CPG Infobase, ProQuest Dissertations and Theses Global) were searched in May 2021 to identify relevant systematic reviews and meta-analyses. Four databases were searched again in September 2021 to identify recent primary literature. Non-contact ACL injury data were extracted to calculate incidence rate ratios (IRRs) and these were combined using an inverse variance random-effects model. A qualitative assessment of included reviews was performed. The methodological quality of the studies was assessed using a Measurement Tool to Assess Systematic Reviews 2 (AMSTAR 2) or Cochrane Risk-of-Bias Tool for Randomized Trials (RoB 2). Sixteen reviews and two primary studies met the inclusion criteria. Across 11 primary studies, prevention programs were effective in reducing non-contact ACL injuries by 64% (IRR = 0.36 (95% CI: 0.18-0.70)). A multi-faceted exercise program, beginning in the pre-season and containing at least three exercise types, may be beneficial in reducing ACL injury risk.
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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 | 0.010 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.017 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".