Q. why doesn’t headgear prevent concussion? a. no one bothered to ask
Bibliographic record
Abstract
Objective The study identifies barriers to the supply of helmets/headgear/headguards (headgear) that are likely to be effective in preventing concussion. Design Literature review and biomechanical testing. Setting All sports. Outcome measures Sport federation rules on headgear and headgear impact performance with regards to concussion. Main results A review of laws and regulations showed that many sports have not developed relevant performance based technical specifications for headgear. World Rugby performance requirements comprise a drop height of 300 mm and pass level of greater than 200 g in impact energy attenuation tests. Testing of current World Rugby approved headgear showed peak linear headform accelerations (PLA) in the range 400 to 600 g in 200 mm and 300 mm drop tests. The performance of the exemplar models is arguably worse than in similar models tested circa 2000 and exceed informative concussion injury criteria, e.g. PLA<75 g. Combat sports generally do not mandate performance requirements. Similarly, there is a range of performance from good to poor in laboratory-tested combat sport headgear with respect to concussion prevention; e.g. for headgear tested at a 500 mm drop height, PLA ranged from 50 g to 466 g. PLA results were reflected in angular head acceleration performance in impact tests. Other sports will be considered, e.g. ice hockey and American football, where performance standards are mandated. Conclusions The availability of headgear designed to prevent concussion is limited because relevant performance requirements are absent and/or aligned with other head injury prevention goals, e.g. preventing severe head injury. Competing interests None.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.098 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.008 | 0.003 |
| Insufficient payload (model declined to judge) | 0.071 | 0.029 |
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".