The influence of repeated chin bar impacts on the protective properties of full-face mountain biking helmets
Bibliographic record
Abstract
Full-face helmets are designed to protect against head and face injuries during downhill and free-ride mountain biking. This study assessed whether multiple impacts and helmet type are related to the protective properties of full-face helmets. A drop tower fitted with a helmeted headform simulated impacts to the chin following a forwards fall. Four models of full-face mountain biking helmets were tested. Three repeated trials were completed for each helmet at four impact velocities. Outcome variables included head injury criterion score, peak force, and peak acceleration. Peak accelerations for all trials were below the 300 g pass/fail criterion used in some testing standards. Multiple impacts reduced helmet protective properties, most noticeably at the higher impact velocities (increases in impact severity measures ranging from 11% to 22% for low and 17% to 49% for higher impact velocities). However, the effects of multiple impacts were smaller than the differences observed across individual helmet types. Helmet protective properties were associated with local chin bar characteristics, most notably chin bar length at higher impact velocities. Towards the goal of reducing overall head/brain injury risk in cyclists, there may be value in complimentary messaging about the importance of repeated impacts and helmet type on the protective properties of downhill mountain biking helmets.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".