A Method of Evaluating Helmet Rotational Acceleration Protection Using the Kingston Impact Simulator (KIS Unit)
Bibliographic record
Abstract
OBJECTIVE: Helmet use is the primary form of head protection against traumatic brain injury. Although helmet designs have proven to be effective in reducing the incidence of skull fracture and major traumatic brain injury, there is little evidence that helmets protect against concussion. Linear and rotational accelerations are important mechanisms underlying concussion, yet current testing protocols do not account for rotational acceleration. Technical considerations have prevented a valid, accurate, and reproducible testing paradigm. Our objectives were to design a novel helmet-testing methodology that accurately and reliably measures rotational acceleration at injury-relevant impact forces, locations, and planes and to evaluate differences in rotational force protection in commercially available helmets. SETTING: Laboratory study. INTERVENTION: The Kingston Impact Simulator (KIS unit) was used to study 10 commercially available hockey helmets. The rotational acceleration force protection was measured in the horizontal, coronal, and sagittal planes at each of 12 predetermined impact locations. RESULTS: Mean peak unhelmeted and helmeted accelerations at all impact locations and planes ranged from 63 to 28.6 g and from 26.8 to 8.0 g, respectively. The percent reduction in rotational acceleration for all test helmets ranged from 6.4% to 84%. Statistically significant differences in rotational acceleration between manufacturers and within a helmet brand were identified. CONCLUSIONS: KIS is a novel testing methodology that identifies rotation force protection within and between hockey helmet models and manufacturers at different impact location and planes. This information may be useful in improving future helmet design and construction to provide maximal protection against the forces causing concussion.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".