Characterization of the Biomechanical and Situational Aspects of High Magnitude Subconcussive Impacts in Canadian University Football
Bibliographic record
Abstract
Background: Subconcussive impacts are asymptomatic head impacts that induce alterations in the brain after repetitive exposure. Understanding factors that lead to higher magnitude or frequency of subconcussive head impacts in Canadian football is crucial for the development of harm reduction and prevention strategies. Purpose: The objective of this study was to characterize high magnitude subconcussive impacts in Canadian football to identify injurious relationships between player and game-based characteristics, impact magnitude, and frequency. Methods: Eighty-one male university-level football players were equipped with helmet accelerometers to capture linear acceleration (LA), impact frequency, and helmet impact location. Impacts with LA ≥60g were included in the analysis. Video footage from 15 games over three seasons was analyzed to characterize aspects of play including play type, position, closing distance, player technique, tackling versus blocking, and impact type (helmet-to-helmet/body/ground) during these high magnitude impacts. Results: A total of 570 impacts were included in the analysis with a mean LA of 83.44 ± 23.60g. Impacts with a closing distance of ≥10 yards resulted in higher LA compared to <10 yards and occurred most frequently in run plays. Small skill players experienced significantly higher LA than linemen. Helmet-to-body impacts generated significantly higher LA compared to helmet-to-helmet, though helmet-to-helmet occurred more often. Impacts with poor technique had higher LA and occurred more frequently in tackling versus blocking impacts. Conclusion: The results of this work suggest that rules regarding offensive backfield running starts could be adjusted to decrease the risk of impact after a larger closing distance. Behavioral modifications and coaching approaches aimed at improving tackling technique could decrease exposure to high magnitude impacts at the level of the individual player.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".