9.16 Sex-related differences in visual reaction time, oculomotor, and cognitive abilities in varsity athletes with a history of concussion
Bibliographic record
Abstract
Objective To determine if concussion history affects sex-related differences in athletes’ abilities to perform visual reaction time, oculomotor, and cognitive tasks. Design Prospective design. Setting Canadian University. Participants Pre-season baseline data was collected from 133 varsity athletes (79 no concussion history: 36 females, 43 males; 54 history of concussion: 28 females, 26 males). Exclusions included previous visual epileptic seizures, strabismus, and colour blindness. Interventions (or Assessment of Risk Factors) Athletes used a VR-goggle/eye-tracking system (Saccade Analytics, Inc.) to look at targets and perform a Stroop task while seated. Outcome Measures Oculomotor accuracy (saccades), oculomotor reaction time (anti-saccades), and cognitive performance (Stroop total errors). Main Results We observed a significant difference in our cognitive task outcome measure between males and females with a history of concussion whereupon females performed (p=0.015). Further, females with a history of concussion (p=0.037) which may relate to greater concussion exposure for higher performing athletes. There was no significant difference between the sexes, with and without concussion history for the visual reaction and oculomotor tasks. Conclusions This study demonstrates that asymptomatic athletes with and without a previous history of concussion do not show a significant performance difference on visual reaction and oculomotor tasks. Cognitive performance, however, shows sex-related differences which may be due to the nature of the task (males tended to execute the test faster, making more errors). This research provides an example of objective visuomotor and cognitive testing that can be used to further explore sex-related differences in concussion injury effects.
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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 | 0.000 |
| 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) | 0.007 | 0.001 |
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".