Injury Report: Undisclosed Health Events in the National Hockey League and Their Impact on Player Health Research
Bibliographic record
Abstract
Background: Teams in the National Hockey League (NHL) are mandated to publicly disclose health events (inclusive of injury and illness) that result in time loss. If disclosure is deemed to negatively impact a player’s physical well-being, teams can withhold details from the public and report the event as undisclosed . As a result, research utilizing publicly available NHL health data may be underreporting specific health events of interest. Purposes: To (1) identify the incidence and severity of undisclosed health events that result in time loss in the NHL, (2) identify the magnitude of potential bias introduced by the exclusion of undisclosed health events, and (3) identify relevant factors that may influence a team’s decision to disclose health events. Study Design: Descriptive epidemiology study. Methods: Using a retrospective cohort inclusive of NHL players from 2016 to 2023, public access data related to time loss due to injury or illness were collected. Outcome measures included incidence and severity of undisclosed health events, and count/incidence of injury and illness categories with undisclosed health events reclassified. Statistical significance was tested using Poisson and negative binomial regression. Results: Of the 5397 publicly reported health events that resulted in time loss during the NHL regular season across 2016 and 2023, 12.10% (653/5397) were due to undisclosed health events. Specific injury and illness classifications were found to be underreported when adjusting for undisclosed health events ( P < .001). Undisclosed health events were more likely to be reported in the last fifth of the season ( P < .001) and more common for higher profile players ( P < .001), regardless of the stage of the season. The severity of undisclosed health events was highly variable across seasons and did not show any significant trends over season duration. Conclusion: Studies utilizing publicly obtained NHL health data are at risk of underreporting occurrence and misreporting the severity if they do not adjust or account for health event disclosure in their analyses.
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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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".