Subconcussive Head Impacts and Neurocognitive Function Over 3 Seasons of Youth Football
Bibliographic record
Abstract
Objective: To determine the association between repetitive subconcussive head impacts and neurobehavioral outcomes in youth tackle football players. Methods: Using helmet-based sensors, we measured head impacts for 3 consecutive seasons of play in 29 male players age 9-11. Cumulative impact g’s were calculated. Players completed a battery of outcome measures before and after each season, including neuropsychological testing, vestibular-ocular sensitivity, and self- and parent-reported measures of symptoms and attention-deficit hyperactivity disorder (ADHD). Results: Average cumulative impact over 3 seasons was 13 900 g. High-intensity hits predicted worse change for self-reported social adjustment ( P = .001). Cumulative impact did not predict change in any of the outcome measures. History of ADHD, anxiety, and depression predicted worse change for self-reported symptoms and social adjustment, independent of head impacts. When players were stratified into 3 groups based on cumulative impact across all 3 seasons, differences in outcome measures existed prior to the start of the first season. These differences did not further increase over the course of the 3 seasons. Conclusion: Over 3 consecutive seasons of youth tackle football, we found no association between cumulative head impacts and neurobehavioral outcomes. Larger sample sizes and longer follow-up times would further assist in characterizing this relationship.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".