Does Game Participation Impact Cognition and Symptoms in Elite Football Players?
Bibliographic record
Abstract
OBJECTIVE: To measure neurocognitive functioning in college and professional football players after game participation. STUDY DESIGN: Retrospective, cross-sectional cohort design. PARTICIPANTS: Ninety-four male university and professional football players. INTERVENTION: All participants completed Immediate Postconcussion Assessment and Cognitive Testing (ImPACT) testing at baseline, and either at postconcussion (group 1) or postgame (group 2) participation. MAIN OUTCOME MEASURES: Results from the 5 ImPACT composite scores (Verbal Memory, Visual Memory, Visual Motor Speed, Reaction Time and Impulse Control) and Total Symptom Score. RESULTS: Repeated-measures analysis of variance demonstrated a significant main effect for time (improvements) in 3 of 5 domains for the postconcussion group, but no improvements in the postgame group. The postconcussion group presented with significantly improved results on 4 of 5 ImPACT domains compared with the postgame group at the follow-up time interval. CONCLUSIONS: Participation in a football game with potential cumulative head contacts did not yield increased symptoms or cognitive impairment. However, the absence of improvement in cognitive functioning in noninjured football players, which was found in those players who were returned to play after an injury, may suggest that there is a measureable impact as a result of playing football.
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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 | 0.002 |
| 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 | 0.001 | 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".