Concussions in National Hockey League (NHL) players: 5-year video analysis
Bibliographic record
Abstract
Objectives To understand, through video analysis, how playing characteristics and mechanism of injury contribute to concussions in the National Hockey League (NHL). Setting National Hockey League. Design Prospective case series of concussions over a 5 year period of regular season NHL games (2006–2011). Subjects Digital video images of 341 diagnosed concussions of NHL players. Outcome Measures Concussions were analysed and coded using the Heads Up Checklist (HUC). The HUC consists of 17 groups of factors, categorised within three main domains: (1) Physical Event, (2) Equipment, and (3) Game Situation. In addition, player characteristics (ie, height, weight, and position) were also captured. Results During the first 4 years of study period, a common injury mechanism characterised by player-to-player contact and resulting in contact to the head by the shoulder, elbow, or gloves, was identified. Several important characteristics were also discerned: (i) Contact often to the lateral aspect of the head; (ii) Player was often not in possession of the puck; and (iii) No penalty was called on the play. For the 2010–11 season, despite the absolute number of concussions remaining similar to previous years of study, the mechanism of direct contact to the head accounted for fewer concussions observed. Conclusions Based on the results from this longitudinal study, it appears that majority of concussions are a result of direct contact to the head. Initiatives to reduce eliminate specific behaviours appears to have had an impact as fewer direct contacts to the head were identified. Competing interests One of the authors (PC) is a paid consultant for the National Hockey League Players' Association (NHLPA).
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".