Ice Hockey Summit II: Zero Tolerance for Head Hits and Fighting
Bibliographic record
Abstract
OBJECTIVE: To present currently known basic science and on-ice influences of sport related concussion (SRC) in hockey, building upon the Ice Hockey Summit I action plan (2011) to reduce SRC. METHODS: The prior summit proceedings included an action plan intended to reduce SRC. As such, the proceedings from Summit I served as a point of departure, for the science and discussion held during Summit II (Mayo Clinic, Rochester MN, October, 2013). Summit II focused on Basic Science of Concussions in Ice Hockey: Taking Science Forward; (2) Acute and Chronic Concussion Care: Making a Difference; (3) Preventing Concussions via Behavior, Rules, Education and Measuring Effectiveness; (4) Updates in Equipment: their Relationship to Industry Standards and (5) Policies and Plans at State, National and Federal Levels to reduce SRC. Action strategies derived from the presentations and discussion described in these sectors were subsequently voted on for purposes of prioritization. The following proceedings include the knowledge and research shared by invited faculty, many of whom are health care providers and clinical investigators. RESULTS: The Summit II evidence based action plan emphasizes the rapidly evolving scientific content of hockey SRC. It includes the most highly prioritized strategies voted on for implementation to decrease concussion. CONCLUSIONS: The highest priority action items identified from the Summit include: 1) eliminate head hits from all levels of ice hockey, 2) change body checking policies, and 3) eliminate fighting in all amateur and professional 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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.031 | 0.014 |
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".