Measuring Objective Brain Vital Signs In Elite Ice Hockey Players
Bibliographic record
Abstract
Optimizing athlete performance has made leaps in the physical domain with teams employing specialists to aid in athlete nutrition and training. Building on this, the importance of cognitive performance in high-level athletes has gained prominence in sports sciences, prompting investigations into the neural underpinnings of athletic excellence. PURPOSE: The present study involved a retrospective analysis of brain vital signs from Junior A ice hockey players to evaluate whether modulation of brain vital signs may be an indicator of cognitive performance optimization. METHODS: Neurophysiological event-related potentials (ERPs) were extracted using the brain vital signs framework, which quantifies auditory sensation (N100), basic attention (P300), and cognitive processing (N400) within a standardized clinical report. Brain vital signs monitoring enabled point-of-care evaluation in 348 athletes for 17 teams across an elite ice hockey league as a baseline scan (as well as 34 players taking part in an All-Star Combine scan). A retrospective analysis focused on neurophysiological differences across competitive environments (All-Star Combine), cognitive training groups, and different player positions. All normally distributed data were evaluated with multivariate analysis of variance testing. Non-normal data were evaluated with the Kruskal-Wallis test. RESULTS: The competitive environment scans revealed significantly reduced N400 latency for forwards versus defensemen (397 ± 62 ms vs. 456 ± 69 ms, p < 0.05) that were not present in baseline scans. Cognitive training analyses revealed a multivariate main effect of group (p < 0.05), driven by the performance training group exhibiting a greater N400 amplitude than the untrained control group (3.0 ± 0.7uV vs. 2.1 ± 0.8uV, p < 0.05). The league-wide scans revealed a main effect of player position (p < 0.05). This revealed significant effects of position on N100 latency (p < 0.05) and P300 amplitude (p < 0.05). CONCLUSION: The current study reveals potential to enhance cognitive performance in elite, high-contact sport athletes, informing cognitive demands and player safety strategies. Objective neurophysiological evaluation offers diverse avenues for performance training and optimization.
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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) | 0.000 | 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) | 0.001 | 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".