Associations between the circumstances and severity of head impacts in men’s university ice hockey
Bibliographic record
Abstract
Improved evidence on the most common and severe types of head impacts in ice hockey can guide efforts to preserve brain health through improvements in protective gear, rink design, player training, and rules of play. In this observational cohort study of men's university hockey, we compared video evidence on the circumstances of 234 head impacts to measures of head impact severity (peak linear accelerations and rotational velocities) from helmet-mounted sensors (GForceTracker). Videos were analyzed with a validated questionnaire, and paired with helmet sensor data. Shoulder-to-head impacts were more common than hand- or elbow-, but there were no differences in head impact severity between upper limb contact sites (p ≥ 0.2). Head-to-glass impacts were nearly four times more common, and just as severe as head-to-board impacts (p ≥ 0.4). Head impacts resulting in major penalties (versus no penalty), or visible signs of concussion (versus no signs), involved greater head rotational velocities (p = 0.038 and 0.049, respectively). Head impacts occurred most often to the side of the head, along the boards to players in their offensive zone without puck possession. Head impact severity did not differ between cases where the head was (versus was not) the primary site of contact (p ≥ 0.6). Furthermore, penalties were called in only 4% of cases where the head was the initial point of contact. Accordingly, rules that focus on primary targeting of the head, while important and in need of improved enforcement, offer a limited solution.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".