A systematic video analysis of National Hockey League (NHL) concussions, part I: who, when, where and what?
Bibliographic record
Abstract
BACKGROUND: Although there is a growing understanding of the consequences of concussions in hockey, very little is known about the precipitating factors associated with this type of injury. AIM: To describe player characteristics and situational factors associated with concussions in the National Hockey League (NHL). METHODS: Case series of medically diagnosed concussions for regular season games over a 3.5-year period during the 2006-2010 seasons using an inclusive cohort of professional hockey players. Digital video records were coded and analysed using the Heads Up Checklist. RESULTS: Of 197 medically diagnosed concussions, 88% involved contact with an opponent. Forwards accounted for more concussions than expected compared with on-ice proportional representation (95% CI 60 to 73; p=0.04). Significantly more concussions occurred in the first period (47%) compared with the second and third periods (p=0.047), with the majority of concussions occurring in the defensive zone (45%). Approximately 47% of the concussions occurred in open ice, 53% occurred in the perimeter. Finally, 37% of the concussions involved injured players' heads contacting the boards or glass. CONCLUSIONS: This study describes several specific factors associated with concussions in the NHL, including period of the game, player position, body size, and specific locations on the ice and particular situations based on a player's position.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".