Factors influencing visor use among players in the National Hockey League (NHL)
Bibliographic record
Abstract
Eye, orbital, and facial injuries are significant risks to National Hockey League (NHL) players, and can be mitigated by the use of a partial visor - currently optional for all non-rookie players. The goal of the current study was to determine the overall use of visors among non-rookie NHL players in the 2013-2014 season and assess factors influencing their uptake. This was an observational, cross-sectional study using active NHL rosters and demographic information obtained from the official NHL website. Visor use was determined based on in-game video or images at two different time points in the 2013-2014 season. The use of visors during the 2013-2014 season was 75.2% among non-rookie players. When rookies were included, the overall use of visors was 77.8%. Compared to Canadian-born players, European players were significantly more likely to choose to wear a visor (odds ratio [OR] 3.48, 95% confidence interval [CI] 1.96-6.17). Players in the younger age-groups, particularly those younger than 24 years (OR 5.67, 95% CI 2.52-5.76) and those between 24 and 28 years (OR 2.18, 95% CI 1.23-3.87), were more likely to wear a visor compared to older players. Overall, visor use continues to grow in the NHL independently of new legislation, and is more likely in younger players and those of European origin.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".