A-19 Distress Predicts Poorer Cognitive Performance At Post-Injury In Concussed Athletes
Bibliographic record
Abstract
Abstract Purpose: This study evaluated the consistency between athletes’ self-reported cognitive symptoms and cognitive test performance. Methods: The sample included 784 Canadian Football League athletes who underwent standardized cognitive testing with cognitive testing (ImPACT) and the Brief Symptom Inventory (BSI-18) at baseline. Over 2 consecutive seasons, 46 players were diagnosed with concussions following assessment with team physicians within 48 hours; 10 were eliminated due to missing data. Five cognitive symptoms (feeling slowed down, feeling in a fog, not feeling right, difficulties concentration, difficulties with memory) were used to create a composite score (Cog). The Cog index, the depression, anxiety, total index scores from the BSI, and the 5 ImPACT cognitive domains were correlated at baseline and post-injury. History of learning disabilities, ADHD, psychiatric diagnosis, and history of concussion were co-variates. Results: There were no significant correlations between Cog, depression, anxiety or total BSI scores and the ImPACT cognitive domains at baseline. At post injury there were no significant correlations between Cog or ImPACT index scores. In contrast, significant negative correlations were found between BSI Anxiety and verbal memory (r = 0.67, p < 0.01), visual memory (r = 0.68, p < 0.01), and visual-motor processing speed (r = 0.48, p < 0.05). In addition, there were significant negative correlations between the BSI total score and visual memory (r = 0.51, p < 0.05) and reaction time (r = 0.55, p < 0.05). Conclusions: Self-reported cognitive difficulties do not correlate with poorer cognitive outcomes in our sample of professional football players. However, distress at post-injury was significantly correlated to performance, suggesting psychological variables may play a role in cognitive functioning.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".