Player and Game Characteristics and Head Impacts in Female Youth Ice Hockey Players
Bibliographic record
Abstract
CONTEXT: Despite the growing popularity of ice hockey among female youth and interest in the biomechanics of head impacts in sport, the head impacts sustained by this population have yet to be characterized. OBJECTIVES: To describe the number of, biomechanical characteristics of, and exposure to head impacts of female youth ice hockey players during competition and to investigate the influences of player and game characteristics on head impacts. DESIGN: Cohort study. METHODS: Twenty-seven female youth ice hockey players (mean age = 12.5 ± 0.52 years) wore instrumented ice hockey helmets during 66 ice hockey games over a 3-year period. Data specific to player, game, and biomechanical head impact characteristics were recorded. A multiple regression analysis identified factors most associated with head impacts of greater frequency and severity. RESULTS: A total of 436 total head impacts were sustained during 6924 minutes of active ice hockey participation (0.9 ± 0.6 impacts per player per game; range, 0-2.1). A higher body mass index (BMI) significantly predicted a higher number of head impacts sustained per game (P = .008). Linear acceleration of head impacts was greater in older players and those who played the forward position, had a greater BMI, and spent more time on the ice (P = .008), whereas greater rotational acceleration was present in older players who had a greater BMI and played the forward position (P = .008). During tournament games, increased ice time predicted increased severity of head impacts (P = .03). CONCLUSIONS: This study reveals for the first time that head impacts are occurring in female youth ice hockey players, albeit at a lower rate and severity than in male youth ice hockey players, despite the lack of intentional body checking.
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) | 0.002 | 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".