What strategies can be used to effectively reduce the risk of concussion in sport? A systematic review
Bibliographic record
Abstract
AIM OR OBJECTIVE: To examine the effectiveness of concussion prevention strategies in reducing concussion risk in sport. DESIGN: Systematic review according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) guidelines. DATA SOURCES: Eleven electronic databases searched and hand-search of references from selected studies. ELIGIBILITY CRITERIA FOR SELECTING STUDIES: The following were the study inclusion criteria: (1) contained original human research data; (2) investigated an outcome of concussion or head impact; (3) evaluated a concussion prevention intervention; (4) included sport participants; (5) analytical study designand (6) peer-reviewed. The following were the exclusion criteria: (1) review articles, case series or case studies and (2) not in English. RESULTS: The studies selected (n=48) provided evidence related to protective gear (helmets, headgear, mouthguards) (n=25), policy and rule changes (n=13) and other interventions (training, education, facilities) (n=10). Meta-analyses demonstrate a combined effect of a 70% reduction (incidence rate ratio (IRR)=0.3 (95% CI: 0.22 to 0.41)) in concussion risk in youth ice hockey leagues where policy disallows body checking, and the point estimate (IRR=0.8 (95% CI: 0.6 to 1.1)) suggests a protective effect of mouthguards in contact and collision sport (basketball, ice hockey, rugby). SUMMARY/CONCLUSIONS: Highlights include a protective effect of helmets in skiing/snowboarding and the effectiveness of policy eliminating body checking in youth ice hockey. Future research should examine mouthguards in contact sport, football helmet padding, helmet fit in collision sport, policy limiting contact practice in youth football, rule enforcement to reduce head contact in ice hockey and soccer, ice surface size and board/glass flexibility in ice hockey and training strategies targeting intrinsic risk factors (eg, visual training). SYSTEMATIC REVIEW REGISTRATION: PROSPERO 2016:CRD42016039162.
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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 | 0.020 | 0.071 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.012 |
| Bibliometrics | 0.013 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| 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".