Effect of Neck Strength on Simulated Head Impacts During Falls in Female Ice Hockey Players
Bibliographic record
Abstract
International Journal of Exercise Science 14(1): 446-461, 2021. This study examined the effect of isometric cervical strength and impact location of the hockey helmet in mitigating the risk of concussions for two different mechanisms of injury from a fall during head impact simulation testing. Isometric cervical strength was measured on 25 female hockey players to compute and model neck strength on a mechanical neckform. A dual-rail vertical drop system with a helmet mounted on a surrogate headform simulated the mechanisms of injury causing concussions on female ice hockey players. Measures of peak linear acceleration and risk of injury due to a head collision (GSI) were used to assess the magnitude of the head impact due to a fall across three neck strength measures (weak, average, strong), three helmet locations (front, rear, side), and two mechanisms of injury (direct, whiplash+impact). A three-way ANOVA revealed a significant main effect for impact mechanism on the magnitude of peak linear acceleration and GSI, with the whiplash+impact mechanism generating significantly greater peak linear acceleration and GSI than the direct impact mechanism. A significant two-way interaction effect was found between impact location and mechanism of injury on peak linear acceleration measures, with the direct impact on the side location generating significantly greater peak linear acceleration than the frontal location. On the contrary, the whiplash+impact mechanism revealed that the frontal impact location produced significantly greater peak linear acceleration than the side location. This outcome suggests the geometry of the helmet material and the type of mechanism of injury both play a role in concussion risk.
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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 | 0.000 | 0.001 |
| 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".