The end game: Mortality outcomes in North American professional athletes
Bibliographic record
Abstract
Comprehensive investigations into the mortality outcomes of elite athletes can assist in decoding risk factors for premature mortality and provide avenues for exploring human health through engagement in sport. As such, the purpose of this study was to comprehensively examine lifespan trends of athletes from the 4 major sports in North America: Major League Baseball (MLB), National Basketball Association (NBA), National Football League (NFL), and National Hockey League (NHL). We hypothesized that proportional death rates would be similar across the 4 sports, when standardizing the data by debut years. Overall, 17 523 of 50 515 (34.7%) athletes were deceased as of the respective data collection cutoff date for their sport, with MLB players having the highest risk of imminent mortality. Professional basketball players generally had the highest relative proportion of death when standardizing data by debut year, although NHL and NFL players who debuted after 2005 had the highest proportion of death. In addition, a 1-year increase in career length significantly decreased the risk of death (HR: 0.982, 95% CI: 0.978-0.985), even after adjusting for sport type (HR: 0.977, 95% CI: 0.974-0.980). Meaningful significance should be considered given the historical and unique nature of the sample. Nevertheless, investigating risk of death differences through different occupational and biological variables can help highlight aversive trends to lifespan that permeate throughout high-performance athlete populations.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".