7.17 Sex-related differences in the effect of concussion history on cognitive-motor integration performance in varsity athletes
Bibliographic record
Abstract
Objective Examine the interaction between sex, concussion history, and skilled performance. Design Prospective. Setting University Clinic. Participants Varsity athletes, 38 with concussion history (18 female), 37 with no history (27 female). Assessment Participants were tested on two visuomotor skill tasks where they slid a finger across a vertically-oriented touch screen to targets viewed on that same screen (standard, condition S) or on a second horizontally-oriented screen with cursor viewed on the vertically-oriented screen and feedback reversed (e.g., slide finger right on horizontal screen to move cursor left on other screen, requiring cognitive-motor integration, condition CMI). Condition order was randomized across participants. Outcome Measures Kinematic variables: percentage of errors, reaction time, movement time, finger pathlength, precision, accuracy, and peak velocity. Results In males, concussion history significantly impacted performance on accuracy (p=0.016) in condition CMI, and pathlength in both conditions (p<0.001). Notably, this deficit pattern was not observed in females. A two-way ANOVA showed an interaction of sex and concussion history on accuracy (p=0.009), and peak velocity (p=0.019) in condition CMI, and full pathlength in both condition S (p<0.001) and CMI (p<0.01). Conclusions These results suggest that males and females respond differently to concussive injury and therefore may require different tools for clinical diagnoses, outcome projections, and recovery assessment. Further research will examine the potential interaction of sex-related differences in emotionality symptoms and CMI performance following concussion (seen as a trend in the current data set) in hopes of creating an increasingly specific and accurate tool for concussion diagnosis and recovery tracking.
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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) | 0.008 | 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".