Is protective equipment useful in preventing concussion? A systematic review of the literature
Bibliographic record
Abstract
OBJECTIVE: To determine if there is evidence that equipment use reduces sport concussion risk and/or severity. DATA SOURCES: 12 electronic databases were searched using a combination of Medical Subject Headings and text words to identify relevant articles. REVIEW METHODS: Specific inclusion and exclusion criteria were used to select studies for review. Data extracted included design, study population, exposure/outcome measures and results. The quality of evidence was assessed based on epidemiologic criteria regarding internal and external validity (ie, strength of design, sample size/power calculation, selection bias, misclassification bias, control of potential confounding and effect modification). RESULTS: In total, 51 studies were selected for review. A comparison between studies was difficult due to the variability in research designs, definition of concussion, mouthguard/helmet/headgear/face shield types, measurements used to assess exposure and outcomes, and variety of sports assessed. The majority of studies were observational, with 23 analytical epidemiologic designs related to the subject area. Selection bias was a concern in the reviewed studies, as was the lack of measurement and control for potentially confounding variables. CONCLUSIONS: There is evidence that helmet use reduces head injury risk in skiing, snowboarding and bicycling, but the effect on concussion risk is inconclusive. No strong evidence exists for the use of mouthguards or face shields to reduce concussion risk. Evidence is provided to suggest that full facial protection in ice hockey may reduce concussion severity, as measured by time loss from competition.
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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 | 0.011 | 0.058 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| 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".