Mortality and chronic traumatic encephalopathy (CTE) among enforcers and non-enforcers in the National Hockey League (NHL)
Bibliographic record
Abstract
Objective Many NHL teams roster players whose primary responsibility is fighting with opposing players. Over time, these “enforcers” may experience repetitive head impacts (RHI), a risk factor for serious long-term health consequences including neurodegenerative disease. This study examined whether retired NHL enforcers and non-enforcers differ on two long-term health outcomes. Methods In this matched cohort study conducted with retrospective, publicly available data, cohorts of former NHL enforcers and non-enforcers were compared on mortality, and CTE diagnosis. NHL players were deemed enforcers (ENFs, n = 239) if listed in a Wikipedia piece entitled “List of NHL enforcers.” A randomly selected sample of non-enforcers (non-ENFs, n = 239) were matched to ENFs on year of birth and the first NHL season played. Goalies and players with less than 30 games of NHL experience were excluded. Results The matching procedure resulted in equivalent cohorts with respect to birth year (1969.9) and first NHL season played (1991.3). Significantly more ENFs had died (n = 23, 9.6% vs. n = 9, 3.8%; p = 0.01) and significantly more ENFs had been given a diagnosis of CTE (n = 7, 2.9% vs. n = 1, 0.4%; p < 0.05). While not statistically significant, age at death averaged 9+ years younger among ENFs (mean = 53.6) compared to non-ENFs (mean = 63). Players born in Canada were over-represented in the ENF cohort. Conclusion This study found higher mortality and more diagnoses of CTE in a cohort of enforcers relative to matched non-enforcers. Given expanding evidence linking RHI to life-threatening long-term health impacts, the NHL must protect players and mandate rule changes that minimize or eliminate fighting.
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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 | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".