Vital statistics and early death predictors of North American professional basketball players: A historical examination
Bibliographic record
Abstract
While empirical evidence suggests that elite athletes have superior lifespan outcomes relative to the general population, less is known regarding their causes of death. The purpose of this study was to critically examine the mortality outcomes of deceased National Basketball Association and American Basketball Association players. Death data were collected from publicly available sources until 11 December 2015, and causes of death were categorized using the International Classification of Diseases, Tenth Revision (ICD). Mortality was measured through: i) cause-specific crude death rates (CDR), ii) estimates of death rates per athlete-year (AY), and iii) binary and multinomial regression analyses. We identified 514 causes of death from 787 deceased players (M = 68.1 y ± 16.0) from 16 different ICD groups, 432 of which were from natural causes. Findings showed similar leading causes of death and CDRs to sex- and race-matched controls, higher death rate differences per AY within time-dependent variables (i.e., birth decade, race, and height), and a higher likelihood of dying below the median age of death for black and taller players, although this was highly confounded by birth decade. More complete knowledge of mortality outcomes would provide broad public health applications and disarm harmful stereotypes of elite athlete health.
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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 | 0.001 |
| 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 | 0.001 |
| 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".