Sports concussions: can head impact sensors help biomedical engineers to design better headgear?
Bibliographic record
Abstract
Sport-related concussion is a major public health concern. In a recent BJSM publication, McGuine et al conducted a randomised controlled trial (RCT) to evaluate whether soccer headgear would reduce the rate or severity of concussions in adolescent athletes (nheadgear = 1505, nno_headgear = 1545).1 Both ‘control’ athletes (who wore no helmets) and the athletes who wore soccer headgear had similar concussion rates and recovery times after a concussion. In another study, headgear was also found to be ineffective in reducing concussion rates or recovery times in rugby union.2 Padding may seem like the most intuitive way to protect the head from injury. So why might this approach not be concussion-proof? Adding a foam pad can help ‘soften the blow’, by increasing the loading area and absorbing some of the impact energy. However, given a regular thickness pad, substantial impact energy is still transmitted to the head and produces a sharp head/skull acceleration. The skull acceleration in turn shakes and deforms its contents—the brain. Traditionally designed helmets and padding reduce focal loading and head linear accelerations that are associated with skull fracture risk.3 However, head rotation has been hypothesised to be a …
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.017 | 0.018 |
| Insufficient payload (model declined to judge) | 0.013 | 0.009 |
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".