Collision Type and Player Anticipation Affect Head Impact Severity Among Youth Ice Hockey Players
Bibliographic record
Abstract
OBJECTIVE: The objective was to determine how body collision type and player anticipation affected the severity of head impacts sustained by young athletes. For anticipated collisions, we sought to evaluate different body position descriptors during delivery and receipt of body collisions and their effects on head impact severity. We hypothesized that head impact biomechanical features would be more severe in unanticipated collisions and open-ice collisions, compared with anticipated collisions and collisions along the playing boards, respectively. METHODS: Sixteen ice hockey players (age: 14.0 + or - 0.5 years) wore instrumented helmets from which biomechanical measures (ie, linear acceleration, rotational acceleration, and severity profile) associated with head impacts were computed. Body collisions observed in video footage captured over a 54-game season were evaluated for collision type (open ice versus along the playing boards), level of anticipation (anticipated versus unanticipated), and relative body positioning by using a new tool developed for this purpose. RESULTS: Open-ice collisions resulted in greater head linear (P = .036) and rotational (P = .003) accelerations, compared with collisions along the playing boards. Anticipated collisions tended to result in less-severe head impacts than unanticipated collisions, especially for medium-intensity impacts (50th to 75th percentiles of severity scores). CONCLUSION: Our data underscore the need to provide players with the necessary technical skills to heighten their awareness of imminent collisions and to mitigate the severity of head impacts in this 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 | 0.000 | 0.002 |
| 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) | 0.002 | 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".