Prevention strategies and modifiable risk factors for concussion: a systematic review and meta-analysis for the Female, woman and girl/or Athlete Injury pRevention (FAIR) consensus
Bibliographic record
Abstract
OBJECTIVE: To examine prevention strategies and potential modifiable risk factors (MRFs) for sport-related concussion (SRC) and head impact/head acceleration event (HAE) outcomes in female, woman and/or girl athletes. DESIGN: Systematic review with meta-analyses and Grading of Recommendations, Assessment, Development and Evaluation. DATA SOURCES: Medline, CINAHL, PsycINfo, SportDiscus, ERIC, CENTRAL and CDSR. ELIGIBILITY: Primary data studies with comparison group(s) assessing the association of prevention interventions and/or MRFs for SRC or HAE with ≥1 female/woman/girl in each study group. RESULTS: Of the 108 included studies, 67 evaluated a SRC prevention strategy (equipment n=25, policy/rule n=21, training n=10, management n=11) and 41 evaluated potential MRFs (34 distinct MRFs across nine categories). In total, 40/108 (37%) studies (prevention 19/67; MRF 21/41) included female/woman/girl-specific estimates. Three meta-analyses were conducted: two SRC prevention strategies (headgear, eyewear) and one MRF (artificial turf vs grass) based on availability of female/woman/girl-only estimates and similar outcomes and exposure. Headgear was associated with 30% lower SRC rates in adolescent female/girl lacrosse and soccer (IRR=0.70, 95% CI 0.50 to 0.99; very-low certainty). Eyewear use was not protective for SRC (IRR=1.08, 95% CI 0.69 to 1.68; very-low certainty). SRC rates did not differ by artificial turf versus grass (IRR=0.95, 95% CI 0.62 to 1.45; very-low certainty). CONCLUSION: We found limited evidence for prevention strategies and MRFs in female/woman/girl athletes except for very-low certainty evidence supporting headgear use in adolescent lacrosse and soccer. Future studies should consider the design, implementation and evaluation of SRC prevention strategies that target MRFs to guide safe practice recommendations specifically for female/woman/girl athletes.
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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 | 0.021 | 0.047 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.034 |
| Bibliometrics | 0.009 | 0.006 |
| 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".