Long-term electrophysiological changes in athletes with a history of multiple concussions
Bibliographic record
Abstract
PRIMARY OBJECTIVE: This event-related potentials study investigated the long-term effects associated with a history of one or multiple concussions on the N2pc and P3 components using a visual search oddball paradigm. METHODS AND PROCEDURE: A total of 47 university football players were assigned to three experimental groups based on prior concussion history: Athletes with a history of one concussion (single-concussion group); Athletes with two or more concussions (multi-concussion group); non-concussed athletic controls. The average post-concussion period was 31 months for athletes in the multi-concussion group and 59 months for the single-concussion group. RESULTS: This study found significantly suppressed P3 amplitude in the multi-concussed athletes group compared to the single-concussion and non-concussed athletes even when using the time since the latest concussion as a covariate. CONCLUSION: This finding suggests that the multi-concussed athletes group showed long-lasting P3 amplitude suppression when compared with single-concussion or non-concussed athletes despite equivalent neuropsychological test scores and post-concussion symptoms self-reports. This pattern of results is important because it shows that 'old' concussions do not cause general or ubiquitous electrophysiological suppression. The specificity of the long-term effects of previous concussions to the P3, along with an intact N2pc response, suggests that further work may allow one to pinpoint the cognitive system that is specifically affected by multiple concussions.
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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 | 0.000 | 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) | 0.002 | 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".