Brain Vital Signs in Elite Ice Hockey: Towards Characterizing Objective and Specific Neurophysiological Reference Values for Concussion Management
Bibliographic record
Abstract
Background:Prior concussion studies have shown that objective neurophysiological measures are sensitive to detecting concussive and subconcussive impairments in youth ice-hockey. These studies monitored brain vital signs at rink-side using a within-subjects design to demonstrate significant changes from pre-season baseline scans. However, practical clinical implementation must overcome inherent challenges related to any dependence on a baseline. This requires establishing the start of normative reference data sets. Methods:The current study collected specific reference data forN= 58 elite, youth, male ice-hockey players and compared these with a general reference dataset fromN= 135 of males and females across the lifespan. The elite hockey players were recruited to a select training camp through CAA Hockey, a management agency for players drafted to leagues such as the National Hockey League (NHL). The statistical analysis included a test-retest comparison to establish reliability, and a multivariate analysis of covariance to evaluate differences in brain vital signs between groups with age as a covariate. Findings:Test-retest assessments for brain vital signs evoked potentials showed moderate-to-good reliability (Cronbach’s Alpha > 0.7, Intraclass correlation coefficient > 0.5) in five out of six measures. The multivariate analysis of covariance showed no overall effect for group (p= 0.105), and a significant effect of age as a covariate was observed (p< 0.001). Adjusting for the effect of age, a significant difference was observed in the measure of N100 latency (p= 0.022) between elite hockey players and the heterogeneous control group. Interpretation:The findings support the concept that normative physiological data can be used in brain vital signs evaluation in athletes, and should additionally be stratified for age, skill level, and experience. These can be combined with general norms and/or individual baseline assessments where appropriate and/or possible. The current results allow for brain vital sign evaluation independent of baseline assessment, therefore enabling objective neurophysiological evaluation of concussion management and cognitive performance optimization in ice-hockey.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".