An observational method to code concussions in the National Hockey League (NHL): the heads-up checklist
Bibliographic record
Abstract
BACKGROUND: Development of effective strategies for preventing concussions is a priority in all sports, including ice hockey. Digital video records of sports events contain a rich source of valuable information, and are therefore a promising resource for analysing situational factors and injury mechanisms related to concussion. AIM: To determine whether independent raters reliably agreed on the antecedent events and mechanisms of injury when using a standardised observational tool known as the heads-up checklist (HUC) to code digital video records of concussions in the National Hockey League (NHL). METHODS: The study occurred in two phases. In phase 1, four raters (2 naïve and 2 expert) independently viewed and completed HUCs for 25 video records of NHL concussions randomly chosen from the pool of concussion events from the 2006-2007 regular season. Following initial analysis, three additional factors were added to the HUC, resulting in a total of 17 factors of interest. Two expert raters then viewed the remaining concussion events from the 2006-2007 season, as well as all digital video records of concussion events up to 31 December 2009 (n=174). RESULTS: For phase 1, the majority of the factors had a κ value of 0.6 or higher (8 of 15 factors for naïve raters; 11 of 15 factors for expert raters). For phase 2, all the factors had a total percent agreement value greater than 0.8 and κ values of >0.65 for the expert raters. CONCLUSIONS: HUC is an objective, reliable tool for coding the antecedent events and mechanisms of concussions in the NHL.
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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 | 0.058 | 0.126 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".