The Effects of Clinical, and Sub‐Clinical mTBI on Cognitive Function in Varsity Athletes
Bibliographic record
Abstract
Emerging research suggests that head trauma, causing clinical (medically diagnosed, ie. concussion), or sub‐clinical minor Traumatic Brain Injury (mTBI) is cumulative and correlates negatively with cognitive function. Given the high prevalence of head trauma in contact athletics, better understanding the relationship between contact‐athletic activity, mTBI, and independent aspects of cognitive function is imperative. Sixty‐eight varsity football athletes were recruited to examine how cumulative head trauma and self‐reported mTBI affect cognitive function. Participants completed a 12‐test online cognitive battery ( www2.cbstrials.com ) at time points prior to, bi‐weekly throughout, and after the season. With each cognitive test, participants also completed a personal history questionnaire to assess their ongoing athletic activity and mTBI status. Through assessing three distinct aspects of cognitive function (verbal ability, short‐term memory, and reasoning ability) via the cognitive battery in a longitudinal fashion, we will comprehensively examine the effects of contact athletic participation on independent aspects of cognitive function. Results look to better characterize the relationship between mTBI and cognitive function as well as inform sport decisions made by athletes, recreationalists and athletic governance councils to guide participation and safe play decisions.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".