A systematic video analysis of National Hockey League (NHL) concussions, part II: how concussions occur in the NHL
Bibliographic record
Abstract
BACKGROUND: Concussions in sports are a growing cause of concern, as these injuries can have debilitating short-term effects and little is known about the potential long-term consequences. This work aims to describe how concussions occur in the National Hockey League. METHODS: Case series of medically diagnosed concussions for regular season games over a 3.5-year period during the 2006-2010 seasons. Digital video records were coded and analysed using a standardised protocol. RESULTS: 88% (n=174/197) of concussions involved player-to-opponent contact. 16 diagnosed concussions were a result of fighting. Of the 158 concussions that involved player-to-opponent body contact, the most common mechanisms were direct contact to the head initiated by the shoulder 42% of the time (n=66/158), by the elbow 15% (n=24/158) and by gloves in 5% of cases (n=8/158). When the results of anatomical location are combined with initial contact, almost half of these events (n=74/158) were classified as direct contact to the lateral aspect of the head. CONCLUSIONS: The predominant mechanism of concussion was consistently characterised by player-to-opponent contact, typically directed to the head by the shoulder, elbow or gloves. Also, several important characteristics were apparent: (1) contact was often to the lateral aspect of the head; (2) the player who suffered a concussion was often not in possession of the puck and (3) no penalty was called on the play.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".