Measurement of Head Impacts in Youth Ice Hockey Players
Bibliographic record
Abstract
Despite growing interest in the biomechanical mechanisms of sports-related concussion, ice hockey and the youth sport population has not been studied extensively. The purpose of this pilot study was: 1) to describe the biomechanical measures of head impacts in youth minor ice hockey players; and, 2) to investigate the influence of player and game characteristics on the number and magnitude of head impacts. Data was collected from 13 players from a single competitive Bantam boy's (ages 13-14 years) AAA ice hockey team using telemetric accelerometers implanted within the players' helmets at 27 ice hockey games. The average linear acceleration, rotational acceleration, Gadd Severity Index and Head Injury Criterion of head impacts were recorded. A significantly higher number of head impacts per player per game were found for wingers when compared to centre and defense player positions (df=355, t=3.087, p=0.00218) and for tournament games when compared to regular season and playoff games (df=355, t=2.641, p=0.086). A significant difference in rotational acceleration according to player position (F2,1812=4.9551, p=0.0071) was found. This study is an initial step towards a greater understanding of head impacts in youth ice hockey.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".