What are the most effective risk-reduction strategies in sport concussion?
Bibliographic record
Abstract
AIM: To critically review the evidence to determine the efficacy and effectiveness of protective equipment, rule changes, neck strength and legislation in reducing sport concussion risk. METHODS: Electronic databases, grey literature and bibliographies were used to search the evidence using Medical Subject Headings and text words. Inclusion/exclusion criteria were used to select articles for the clinical equipment studies. The quality of evidence was assessed using epidemiological criteria regarding internal/external validity (eg, strength of design, sample size/power, bias and confounding). RESULTS: No new valid, conclusive evidence was provided to suggest the use of headgear in rugby, or mouth guards in American football, significantly reduced players' risk of concussion. No evidence was provided to suggest an association between neck strength increases and concussion risk reduction. There was evidence in ice hockey to suggest fair-play rules and eliminating body checking among 11-years-olds to 12-years-olds were effective injury prevention strategies. Evidence is lacking on the effects of legislation on concussion prevention. Equipment self-selection bias was a common limitation, as was the lack of measurement and control for potential confounding variables. Lastly, helmets need to be able to protect from impacts resulting in a head change in velocities of up to 10 and 7 m/s in professional American and Australian football, respectively, as well as reduce head resultant linear and angular acceleration to below 50 g and 1500 rad/s(2), respectively, to optimise their effectiveness. CONCLUSIONS: A multifactorial approach is needed for concussion prevention. Future well-designed and sport-specific prospective analytical studies of sufficient power are warranted.
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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 | 0.019 | 0.112 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".