P.185 Impact-detecting helmets as indicators of concussion and blood brain barrier integrity in university football players
Bibliographic record
Abstract
Background: Repetitive sub-concussive head impacts have been associated with changes in brain architecture and neurological symptoms. In this study, we examined the association between repetitive sub-concussive impacts, impact burden, and blood brain barrier (BBB) integrity in university football players. Methods: 59 university football players were followed over the 2019 season. Athletes with diagnosed concussion and those sustaining impacts that alerted a sideline impact monitor (relayed by ferroelectric helmet sensors) underwent dynamic contrast-enhanced MRI (DCE-MRI) within one week of injury/alert, and 4 weeks following initial incident. Results: Helmets recorded 2648 impacts over 48 cumulative hours. 8 concussions occurred during the 2019 season (2.82 per 1000 activity hours). On average, athletes with a diagnosed concussion had 55.3 impacts to the front sensor, compared to 14.1 in non-concussed athletes. Athletes who consented to DCE-MRI (n=5) had 10.78% BBB-D within a week of concussion/alert, and 6.77% BBB-D at 4-weeks. Conclusions: We show quantification of BBB integrity relative to head impact burden for the first time. This preliminary study highlights the potential of impact-detecting helmets to provide relevant impact characteristics and offers a foundation for future work on neurological consequences of repetitive sub-concussive impacts.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".