Concussion in Ice Hockey
Bibliographic record
Abstract
OBJECTIVE: This review provides an update on sport-related concussion (SRC) in ice hockey and makes a case for changes in clinical concussion evaluation. Standard practice should require that concussions be objectively diagnosed and provide quantitative measures of the concussion injury that will serve as a platform for future evidence-based treatment. METHODS: The literature was surveyed to address several concussion-related topics: research in ice hockey-related head trauma, current subjective diagnosis, promising components of an objective diagnosis, and current and potential treatments. MAIN RESULTS: Sport-related head trauma has marked physiologic, pathologic, and psychological consequences for athletes. Although animal models have been used to simulate head trauma for pharmacologic testing, the current diagnosis and subsequent treatment in athletes still rely on an athlete's motivation to report or deny symptoms. Bias-free, objective diagnostic measures are needed to guide quantification of concussion severity and assessment of treatment effects. Most of the knowledge and management guidelines of concussion in ice hockey are generalizable to other contact sports. CONCLUSIONS: There is a need for an objective diagnosis of SRC that will quantify severity, establish a prognosis, and provide effective evidence-based treatment. Potential methods to improve concussion diagnosis by health care providers include a standardized concussion survey, the King-Devick test, a quantified electroencephalogram, and blood analysis for brain cell-specific biomarkers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".