Career Head Impact Exposure Profile of Canadian University Football Players
Bibliographic record
Abstract
This study quantified head impact exposures for Canadian university football players over their varsity career. Participants included 63 players from one team that participated in a minimum of 3 seasons between 2013 and 2018. A total of 127,192 head impacts were recorded from 258 practices and 65 games. The mean (SD) number of career impacts across all positions was 2023.1 (1296.4), with an average of 37.1 (20.3) impacts per game and 7.4 (4.4) impacts per practice. The number of head impacts that players experienced during their careers increased proportionally to the number of athletic exposures (P < .001, r = .57). Linebackers and defensive and offensive linemen experienced significantly more head impacts than defensive backs, quarterbacks, and wide receivers (P ≤ .014). Seniority did not significantly affect the number of head impacts a player experienced. Mean linear acceleration increased with years of seniority within defensive backs and offensive linemen (P ≤ .01). Rotational velocity increased with years of seniority within defensive backs, defensive and offensive linemen, running backs, and wide receivers (P < .05). These data characterize career metrics of head impact exposure for Canadian university football players and provide insights to reduce head impacts through rule modifications and contact regulations.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".