51 (5B) Instrumented mouthguard-measured head kinematics and associations with head injury assessment outcomes in elite men’s and women’s Australian football
Bibliographic record
Abstract
Purpose To compare instrumented mouthguard (iMG)-measured head kinematics between elite Australian football players who undergo a head injury assessment (HIA), and are either medically cleared (HIA−) or diagnosed with concussion (HIA+).Methods Head kinematic data were collected over four men’s and women’s seasons, with impacts leading to an HIA verified through video and timekeeper records. Peak linear acceleration (PLA) and peak rotational acceleration (PRA) were compared between HIA− and HIA+ cases.Results Five-hundred and nine men and 612 women had an iMG fitted and participated in at least one season. Twenty-nine HIA+ cases (17 men, 12 women) and 86 HIA− cases (24 men, 62 women) had kinematic data verified. In men, HIA+ cases had higher median PLA than HIA− cases (68 g [interquartile range (IQR): 49–87] vs. 41 g [IQR: 28–71]; p=0.04), with area under the curve (AUC) analysis indicating fair discriminative ability (AUC = 0.69). Similarly, HIA+ cases had higher PRA (6.6 krad/s² [IQR: 4.6–9.0] vs. 3.7 krad/s² [IQR: 2.3–6.2]; p=0.01), with an AUC of 0.73. In women, HIA+ cases had higher median PLA (62 g [IQR: 48–85] vs. 36 g [IQR: 23–55]; p=0.001) and PRA (6.7 krad/s² [IQR: 4.1–8.8] vs. 3.0 krad/s² [IQR: 1.8–4.7]; p=0.001), both with an AUC of 0.78.Conclusion Higher PLA and PRA measurements were observed in athletes diagnosed with concussion compared to those who were cleared following HIA. Ultimately, iMG kinematics may assist in the identification of players at risk of concussion who require medical screening.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".