Comparison of Head Impact Frequency and Magnitude for Midget and Junior Ice Hockey Players to Inform Safety and Policy
Bibliographic record
Abstract
Hockey Canada reports Junior hockey players have a greater incidence of mild traumatic brain injury when compared to Midget hockey players. Very little research has been undertaken to understand the differences in impact events, frequency, and magnitude that contribute to these differences in brain injury incidence. Videos of 20 Midget and Junior ice hockey games were analyzed to determine the frequency and magnitude of head impact events. Physical reconstructions of the head impact events were conducted using a Hybrid III headform to obtain the linear and rotational acceleration characteristics. Linear and rotational acceleration data were used for finite element modeling to calculate the peak maximum principal strain within the cerebrum. The results demonstrated that while Midget and Junior ice hockey players had similar head impact frequencies during play (p = 0.0912), Junior players experienced a greater number of total impacts to the head after the whistle (p = 0.0079). When the impacts during play were broken down by event type, punches to the head (p = 0.0192) and shoulders to the head (p = 0.0290) were found to be more frequent at the Junior level. The collected data identified similar magnitudes of head impact but differences in the frequency between Midget and Junior hockey. Enforcing existing rules and forming additional policy and regulations surrounding protective measures for athletes to manage shoulder and punch impacts to the head, both during play and after the whistle, may decrease the player's risk of brain trauma in the sport.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".