Relationships among Risk Factors for Concussion in Minor Ice Hockey
Bibliographic record
Abstract
Abstract There is increasing concern among parents, coaches, and officials about injury risk in youth ice hockey, particularly in light of recent evidence suggesting that incidence of serious injury is considerably under reported. However, an adequate method for ascertaining injury risk for concussion does not yet exist. The purpose of this study was to examine the relationships among variables measuring exposure and head impact forces in a group of representative level bantam aged hockey players. Across an entire hockey season, trained research assistants attended games and recorded the duration of time spent on the ice for each player (i.e., exposure time) and total number of body contacts using time-on-task software designed specifically for this study. A body contact included any intentional or incidental contact between two players. Collectively, these variables provide a simple, easily administered measure of head injury risk for researchers collecting data in this area. However, their relationship to actual brain trauma is unknown. To this end, head acceleration data were also collected using helmet-based accelerometers that provide measures of linear accelerations experienced by each player. These data were collected by telemetry methods and represent data that are likely very useful for injury researchers but not without sufficient costs. Results demonstrated low associations among the data sources. A method based on combining data sources (through an examination of their potential relationships) is proposed to maximize the potential to identify at-risk youth in minor hockey.
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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 | 0.005 |
| 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 | 0.000 |
| 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".