A-37 Ocular Motor Impairments in Concussion Professional Football Players
Bibliographic record
Abstract
Abstract Purpose: The purpose of this study was to examine baseline and post-concussion injury characteristics of ocular eye movements using the Sync Think visual eye tracking apparatus in professional football players in the Canadian Football League (CFL). Methods: The visual tracking testing protocol uses an integrated stimulus presentation-eye tracking apparatus (EyeLink CL; SR Research, Ontario, Canada) with which eye movements were recorded at 500 Hz using infrared video-oculography. The standard deviation of radial errors (SDRE) and tangential errors (SDTE) were used to determine poor visual tracking. Participants were (n = 748) rostered players in the Canadian Football League. All participants completed assessments at baseline. Sixteen players completed post-concussion assessments and 98 players completed post-exertional assessments during the 2021 season to evaluate the impact of physical exertion on ocular movement metrics. Results: Participants with diagnosed concussions (n = 16+) demonstrated significantly poorer performance on measures of both gaze stability and smooth pursuit (p < 0.001). The poorer results were correlated with significantly higher subjective ratings of dizziness (p < 0.01) and fogginess (p < 0.001). In contrast, healthy participants who completed the same ocular motor assessments demonstrated no significant differences compared to baseline test performance. Conclusions: Findings suggest that subtle differences in oculomotor functioning arise from sport concussion in professional football players. Further research with larger sample sizes and correlation with other cognitive and physical outcomes is warranted.
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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) | 0.001 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".