Does Visual Performance Influence Head Impact Severity Among High School Football Athletes?
Bibliographic record
Abstract
OBJECTIVE: To compare the odds of sustaining moderate and severe head impacts, rather than mild, between high school football players with high and low visual performance. DESIGN: Prospective quasi-experimental. SETTING: Clinical Research Center/On-field. PARTICIPANTS: Thirty-seven high school varsity football players. INTERVENTIONS: Athletes completed the Nike SPARQ Sensory Station visual assessment before the season. Head impact biomechanics were captured at all practices and games using the Head Impact Telemetry System. MAIN OUTCOME MEASURES: Each player was classified as either a high or low performer using a median split for each of the following visual performance measures: visual clarity, contrast sensitivity, depth perception, near-far quickness, target capture, perception span, eye-hand coordination, go/no go, and reaction time. We computed the odds of sustaining moderate and severe head impacts against the reference odds of sustaining mild head impacts across groups of high and low performers for each of the visual performance measures. RESULTS: Players with better near-far quickness had increased odds of sustaining moderate [odds ratios (ORs), 1.27; 95% confidence intervals (CIs), 1.04-1.56] and severe head impacts (OR, 1.45; 95% CI, 1.05-2.01) as measured by Head Impact Technology severity profile. High and low performers were at equal odds on all other measures. CONCLUSIONS: Better visual performance did not reduce the odds of sustaining higher magnitude head impacts. Visual performance may play less of a role than expected for protecting against higher magnitude head impacts among high school football players. Further research is needed to determine whether visual performance influences concussion risk. CLINICAL RELEVANCE: Based on our results, we do not recommend using visual training programs at the high school level for the purpose of reducing the odds of sustaining higher magnitude head impacts.
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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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".