Effect of Repetitive Head Impacts on Saccade Performance in Canadian University Football Players
Bibliographic record
Abstract
OBJECTIVE: Investigate the effect of cumulative head impacts on saccade latency and errors, measured across two successive football seasons. DESIGN: Participants were acquired from a sample of convenience-one Canadian university football team. Head impacts were collected during training camp, practices, eight regular season games, and four playoff games in each season. Saccade measurements were collected at five time points-before and after training camp, at midseason, after regular season, and after playoffs. SETTING: Two seasons following players from a single USports football team during practices and games. PARTICIPANTS: Players who completed a baseline saccade measurement and a minimum of one follow-up measurement were included in the study. A total of 127 players were monitored across two competitive seasons, including 61 players who participated in both seasons. INDEPENDENT VARIABLES: Head impact measurements were collected using helmet-mounted sensors. MAIN OUTCOME MEASURES: Saccade latency and number of errors were measured using high-speed video or electro-oculography. RESULTS: On average, each head impact increased prosaccade latency by 5.16 × 10 -3 ms (95% confidence interval [CI], 2.26 × 10 -4 -1.00 × 10 -2 , P = 0.03) and antisaccade latency by 5.74 × 10 -3 ms (95% CI, 7.18 × 10 -4 -1.06 × 10 -2 , P = 0.02). These latency increases did not decrease between the two seasons; in fact, prosaccade latencies were 23.20 ms longer (95% CI, 19.40-27.14, P < 0.001) at the second season's baseline measurement than the first. The number of saccade errors was not affected by cumulative head impacts. CONCLUSIONS: Repetitive head impacts in Canadian university football result in cumulative declines in brain function as measured by saccade performance. CLINICAL RELEVANCE: Football organizations should consider implementing policies focused on reducing head impacts to improve player safety.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".