Mortality during marathons: a narrative review of the literature
Bibliographic record
Abstract
BACKGROUND: Millions of community-dwelling individuals run marathons each year. There are infrequent deaths, which are often reported widely, and may create unnecessary alarm about the potential risks. Equally, sensible planning for such eventualities is important when staging an event. OBJECTIVE: The aim of the review was to determine the risk of death from running a marathon and the likely location of such deaths in order to inform the public of the likely risks and improve planning for such events. DESIGN: Narrative review. DATA SOURCES: Primary: PubMed. Secondary: contact was made with the organisers and medical teams of specific marathons and online data sought where necessary. ELIGIBILITY CRITERIA FOR SELECTING STUDIES: Studies had to report the number of participants and deaths during, or within 24 hours of completing the marathon. Results relevant to half marathons or ultramarathons or other endurance events, such as triathlons, were not included. Deaths due to terrorist activity were not included. RESULTS: The risk of death estimated by these studies was approximately 0.67 per 100 000 finishers, that is, 1 death per 149 968 participants. From those studies that reported deaths by sex, the rate of male deaths was 0.98/100 000 (1 per 102 503) vs 0.41/100 000 (1 per 243 879) in females. Deaths tended to occur in the last quarter of the race. SUMMARY/CONCLUSION: The risk of death from participating in a marathon is small. Men are more at risk than women. Deaths tend to occur later in the race.
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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 | 0.006 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".