11.7 Head impact biomechanics differ by head location in Canadian high school football players
Bibliographic record
Abstract
Objective To determine head-impact magnitude differences between head-impact locations in high-school tackle football players. Design Cross-sectional. Setting Two Calgary (Canada) high schools. Participants Sixty-eight high-school football players. Assessment of Risk Factors Participants wore instrumented mouthguards (iMG;Prevent Biometrics®) throughout the 2021 season to measure head-impact biomechanics and the location for each head-impact (i.e., front, top, side, rear). Outcome Measures Head-impact variables included peak magnitudes of resultant linear acceleration (g), angular acceleration (rad/s2), linear velocity (m/s), and angular velocity (rad/s). A principal component analysis (PCA) was used to reduce the correlated biomechanical data into PC scores for each head-impact. Results In total, 3,310 head-impacts from 54 players exceeded a threshold of 5g on a single axis and were included in the analysis. The PCA indicated that one PC should be retained, explaining 73.41% of the variance. All four head-impact measurements loaded positively on PC1, meaning greater PC1 scores were associated with greater overall impact accelerations and velocities. Mann-Whitney U tests revealed PC1 scores for side-impacts were the highest, with mean differences of 0.35 units greater than front (p<0.001, 95% CI=[0.20,0.50]), 0.27 units greater than top (p<0.001, 95% CI=[0.07,0.44]), and 0.20 units greater than rear (p=0.001, 95% CI=[0.027,0.37]). Conclusions Side-impacts exhibited the greatest magnitudes of head acceleration and velocity, as determined with iMG data and PCA methods. Further surveillance research is needed to evaluate the relationship between high-impact magnitude locations and concussion rates to inform concussion prevention strategies (e.g., rule, equipment) in youth football.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.007 | 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".