Mechanisms of Injury for Concussions in University Football, Ice Hockey, and Soccer
Bibliographic record
Abstract
OBJECTIVE: To examine the mechanisms of injury for concussions in university football, ice hockey, and soccer. DESIGN: Prospective analysis. SETTING: McGill University. PATIENTS: All athletes participating in varsity football, ice hockey, and soccer. MAIN OUTCOME MEASURES: Athletes participating in university varsity football, ice hockey, and soccer were followed prospectively to determine the mechanisms of injury for concussions, whether certain mechanisms of injury causing concussions were more common in any of the three sports, whether different areas of the body seem to be more vulnerable to a concussion after contact, and whether these areas might be predisposed to higher grades of concussion after contact. RESULTS: There were 69 concussions in 60 athletes over a 3-year period. Being hit in the head or helmet was the most common mechanism of injury for all 3 sports. The side/temporal area of the head or helmet was the most probable area to be struck, resulting in concussion for both football and soccer. When examining the body part or object delivering the concussive blow, contact with another player's helmet was the most probable mechanism in football. CONCLUSION: The mechanisms of injury for concussions in football are similar to previously published research on professional football players. The mechanisms of injury for concussions in soccer are similar to past research on Australian rules football and rugby.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".