HEAD IMPACT CHARACTERISTICS IN YOUTH ICE HOCKEY
Bibliographic record
Abstract
Background Few studies have investigated head impacts in youth ice hockey, none of which have reported impact mechanisms. Objective To investigate head impact characteristics in youth ice hockey. Design Video analysis. Setting 2013/2014 Calgary bantam (13–14 years) ice hockey season. Methods A previously compiled video database of 7260 bantam ice hockey player-to-player contacts from 22 games was searched for head impact cases. Eight games were randomly selected, two elite and six non-elite, from which head impact cases were analysed. Results A total of 254 head impact cases were identified, which represented 3.5% of all player-to-player contacts at a rate of 11.5 head contacts per game. A total of 100 head impact cases were analysed. Two-thirds of all cases (67%) occurred in close proximity to the boards and 11% of all cases resulted in a penalty. Over half of all impacts (55%) were to the side of the helmet, followed by the cage (29%), rear (7%), front (6%) and top (2%). The primary impacting object was an opposing player in 69% of all cases with the most common being the shoulder (31%), helmet (12%) and glove (10%). The impacting object was the glass and boards for 17% and 11% of all cases, respectively. A secondary impact occurred in 21% of all cases, which was most commonly to the side of the helmet and impacting the glass. One case involving a tertiary impact was identified, which comprised of two impacts to the shoulder of an opposing player and then an impact against the boards during the subsequent fall. Conclusions Impacts in youth ice hockey games are typically to the side and cage of helmets by an opposing player. Helmet performance and standards testing should include representative impacts by compliant surfaces to simulate player-to-player contact.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.005 | 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".