Injury and Illness Trends in the National Hockey League Following an Abrupt Cessation of Play
Bibliographic record
Abstract
Background: The National Hockey League (NHL) saw an unprecedented disruption to the competitive calendar due to the COVID-19 pandemic in March of 2020. Returning to play following an abrupt cessation of activity is a known risk factor for athletes. Purpose: To analyze the occurrence and severity of events (injury and illness) in the NHL and to understand any differences in occurrence and severity between pre-pandemic seasons and seasons that immediately followed. Study Design: Descriptive Epidemiology Study. Methods: Using a retrospective cohort inclusive of all players on active rosters in the NHL between 2016-2023, public access injury and illness data were collected. Outcome measures included event incidence, period prevalence, and severity (mean days lost; MDL), as well as incidence rate ratio (IRR) comparing pre- and post-pandemic seasons. Results: IRR for illness peaked in December 2021 (IRR = 62.46; 95% CI 13.65 to 285.91). Incidence of upper body injuries was significantly higher in 2020-21 (IRR = 1.70, p = 0.001) and 2021-22 (IRR = 1.40, p = 0.044) compared to pre-pandemic seasons (Incidence = 17.58 injuries / 1000 player-hours). Injury incidence increased as the 2022-23 season progressed (p = 0.004); injury incidence was stable across all other seasons. Mean days lost (MDL) to injury was higher in 2020-21 (MDL = 18.12, p < 0.001), 2021-22 (MDL = 18.46, p = 0.015), and 2022-23 (MDL = 18.12, p < 0.001) compared to pre-pandemic seasons (MDL = 17.34). Conclusion: Incidence of upper body injuries increased in the 2020-21 and 2021-22 NHL regular seasons while it decreased significantly in the 2022-23 regular season compared with the four pre-pandemic seasons. This suggests a need to examine if modifiable risk factors exist for determining optimal return to play strategies following an abrupt cessation of play. Level of Evidence: 3.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".