11.12 Video analysis of concussions in the national hockey league (NHL) over 5 seasons: 2014–2015 to 2018–2019
Bibliographic record
Abstract
Objective To describe the sequence of events leading to concussion in NHL players from 2014–15 to 2018–2019 and compare injury characteristics to prior to Rule 48 (i.e., Illegal Check to the Head). Design Descriptive epidemiological study. Setting National Hockey League (NHL) Video Analysis Program. Digital video records were coded and analysed using a standardised protocol. Participants NHL players diagnosed with concussion where digital video footage of the injury was available (2014–19, n=516; 2006–2010 [before Rule 48], n=189). Outcome Measures Player position, period, location, and sequence of events (e.g., player contact, environment, puck possession, game situation) were analyzed for frequency. Comparison to 2006–2010 data was done with randomly resampling (1000 iterations), akin to a one-sample t-test. Main Results For 2014–19 seasons, sequence of events resulting in concussion were characterized by body checks (to the torso) around the perimeter of the ice. Compared to years 2006–2010, 2014--19 was characterized by significantly fewer concussions following open ice hits (47% vs. 37%; p<0.001), hits to the head via shoulder (42% vs. 31%; p<0.001), fights (9% vs. 3.3%; p<0.001), and lateral hits to head (35% vs. 21%; p<0.001). Concussive events were also less likely to occur in the first period (47% vs. 36%; p<0.001). Conclusions This study describes several differences in the sequences of events leading to concussion during the 2014 – 2019 seasons compared to prior years (2006–2010) predating the implementation of Rule 48.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".