P.113 Neurophysiological effects of repetitive non-concussive impacts in collegiate football players
Bibliographic record
Abstract
Background: Over 1.3 million people in North America participate in tackle football annually. Football players experience a disproportionately higher risk for repetitive non-concussive impacts (NCIs) compared to other high-contact sports athletes. Quantifying how this exposure influences a player’s cognitive function is imperative. While NCIs share the same mechanism as concussions, they do not elicit immediate symptoms. Methods: This study tracked impact exposure in 13 male Queen’s Varsity Football players using six-axis mouthguard accelerometers throughout the season. Electroencephalography (EEG) recordings were conducted at two time points (pre-season and post-season) to measure event-related potentials (ERPs), evaluating auditory sensation, basic attention, and cognitive processing. Results: Analysis of pre- and post-season EEGs revealed group differences in N100 and N400 wave amplitudes but found no correlation between impact exposure metrics (including varying magnitudes, frequencies, and linear and angular accelerations) and deficits in attention or cognitive processing. Conclusions: These findings suggest that a single season of football-related NCIs may not be sufficient to produce detectable changes in cognitive function as measured through ERPs, despite the variation in impact exposure. Further longitudinal studies spanning multiple seasons and additional neurophysiological measures may be necessary to fully understand the cumulative effects of NCIs on cognitive function in football players.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".