Effects of football practice on blink reflex parameters in Division I football athletes
Bibliographic record
Abstract
Objective The objective was to access the effects of football practice sessions on blink reflex parameters using the Eyestat. Background Our laboratory has utilized a potential objective measure to identify concussions. This technology, called Eyestat, is a noninvasive diagnostic tool measuring changes in blink reflex parameters. Prior research cited significant differences in various blink reflex parameters between active play, concussion, and baseline; however, the number of subjects for the active play population was small (N = 10). Thus, the purpose of this study was to identify blink reflex parameters during football practice, providing outcomes potentially helpful with its use for a concussed population. Design/Methods Forty-seven Division I male football players between the ages of 18-22 were evaluated on multiple sessions during a 2 week period of football practices. During various points of the practice, subjects completed the blink test and had their heart rate assessed. During the blink test, subjects placed their face against the apparatus, which directed a puff of air to the corner of the subject’s eye to stimulate the blink. Five puffs were administered in a random fashion over a 20 second period in which videography captured and recorded the blink. Results Results of the study found significant differences in latency (p = 0.0000), time under the threshold (p = 0.0176), oscillations (p = 0.0217) and excursion (p = 0.0003), but no significant differences in differential latency (p = 0.0626). Average heart rate was 125 BPM, with a minimum of 72 BPM and maximum of 172 BPM. The results of this study correlate with prior research in latency, time under the threshold and excursions and support the changes in blink reflex parameters after football practice. Conclusions Future studies need to assess if changes occur with individual physical activity or is it related only to the practice of this sport.
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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 | 0.002 |
| 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) | 0.002 | 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".