Characterising biomechanical and situational aspects of high magnitude subconcussive impacts in Canadian collegiate football
Bibliographic record
Abstract
The objective of this study was to characterise high magnitude subconcussive impacts in Canadian football to identify injurious relationships between player/game-based characteristics, impact magnitude, and frequency. Eighty-one male university-level football players were equipped with helmet accelerometers to capture peak linear acceleration (PLA), impact frequency, and helmet impact location. Impacts with PLA ≥ 60 g were included in this analysis. Video footage from 15 games over three seasons was analysed to characterise aspects of play including play type, position, closing distance, tackling versus blocking, and impact. Impacts occurring at the left outside boundary of the field resulted in significantly higher linear accelerations. Impacts with a closing distance of ≥10 yards resulted in higher PLA compared to <10 yards and occurred most frequently in run plays. Helmet-to-body impacts generated significantly higher PLA compared to helmet-to-helmet, though helmet-to-helmet occurred more often. Players being tackled sustained significantly higher PLAs than those blocking. The results of this work suggest that the left outside boundary is an area of importance for spotters looking to identify high-risk impacts. Further, rules regarding offensive backfield running starts could be adjusted to decrease the risk of impact after a larger closing distance.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".