Mortality in Professional Athletes: Examining Incidence, Predictors and Causes of Death
Bibliographic record
Abstract
Objective: The overarching purpose of this dissertation was to provide an evidence-based portrayal of i) incidence, ii) predictors and iii) causes of death in athletes from Major League Baseball, the National Basketball Association and American Basketball Association, the National Football League and the National Hockey League. More specifically, this investigation highlighted i) mortality outcome differences of athletes between and within professional sport(s), ii) potential statistical artifacts that may be empowering biases of risk of certain lifespan predictors and iii) the challenges of contextualizing historical data to answer questions with relevance in the present where socio-contextual factors may be different. \nMethods: Data on player lifespan and biological and occupational variables were collected from publically available sources. A majority of the data were collected from wikipedia.org and sports-reference.com, which is a recognized sports archive of aggregated athlete records, and were cross-verified through rigorous web-based and sport encyclopedia archival searches. Several methodological approaches were used across seven studies, including descriptive and Kaplan-Meier and Cox regression survival analyses. \nResults: The key findings of this dissertation suggest that elite athletes generally have favourable lifespan outcomes, although numerous characteristics need to be taken into consideration, such as occupational (e.g., required energy system needed for participation) and biological (e.g., height) differences. As well, the leading causes of death in players from the four major sports in North America are similar to the leading causes of death in the age- and sex-matched controls from the Canadian and United States general population. \nConclusions: Statistical limitations and biased reporting may skew public perception of the relationship between participation in high performance sport and lifespan. As such, there is inherent value in scientists critically examining the health outcomes of athletes and to make these data known to a broader audience, particularly as preconceived notions of health risks from sport participation vocalized through media often distort reality and can adversely affect sport participation rates. In summary, a comprehensive understanding of the implications of involvement in elite sport informs our broader understanding of general athlete health and helps to form evidence-based models of athlete development and care.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".