The Effect of Shoulder Pad Design on Head Impact Severity during Checking
Bibliographic record
Abstract
INTRODUCTION: Shoulder-to-head contact is the most common cause of concussions in ice hockey, accounting for 42% of cases in the National Hockey League. The goal of this project was to determine how shoulder pad stiffness, modified by adding foam padding over the shoulder cap of existing shoulder pads, affected head impact severity when participants delivered checks to an instrumented dummy. METHODS: Fifteen participants administered "the hardest shoulder checks they were comfortable delivering" to the head of a dummy equipped with triaxial accelerometers and gyros mounted in its helmet. Trials were conducted with participants wearing two common types of shoulder pads, with and without a 2-cm-thick layer of polyurethane foam over the shoulder pad cap. RESULTS: When participants delivered checks with foam-modified pads versus unmodified pads, there was a decrease of 25.0% in the average peak linear head acceleration (28.73g vs 38.31g, mean difference = 9.58g, 95% confidence interval = 6.35-12.81, P < 0.0001) and a decrease of 12.4% in the average value of peak rotational head velocity (838.0°·s vs 956.7°·s, mean difference = 118.65°·s, 95% confidence interval = 55.37-181.94, P = 0.001). The protective benefit of the foam layer did not depend on the type of shoulder pad or the checking scenario. CONCLUSION: The integration of foam padding on top of the plastic caps of shoulder pads reduced impact severity to the head and warrants further examination as a method for contributing to the prevention of brain injuries in ice hockey.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.002 | 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".