Fifteen risk factors associated with sudden death in Thoroughbred racehorses in North America (2009–2021)
Bibliographic record
Abstract
OBJECTIVE: To identify risk factors associated with race-related sudden death in Thoroughbred racehorses in the US and Canada. ANIMALS: 4,198,073 race starts made by 284,387 Thoroughbred horses at 144 racetracks in the US and Canada between 2009 and 2021. PROCEDURES: Study data were extracted from the Equine Injury Database, which contains detailed records of 92.2% of all official race starts made in the US and Canada during the study period. Forty-nine potential risk factors were analyzed using univariable and multivariable logistic regression. Cases were defined as race starts that resulted in fatality within 3 days of racing, in which at least 1 of 5 codes relating to sudden death was recorded. Fatalities due to catastrophic musculoskeletal injury were omitted from the study cohort. RESULTS: 536 race starts resulted in sudden death, an incidence rate of 0.13/1,000 starts. Fifteen risk factors were significantly associated with sudden death, including horse age and sex, season and purse of race, race distance, and horses' recent history of injury and lay-up. Horses racing while on furosemide medication were at 62% increased odds of sudden death. CLINICAL RELEVANCE: Associations found between previous injury and sudden death suggests preexisting pathology could contribute in some cases. The association between furosemide and sudden death prompts further study to understand which biological processes could contribute to this result.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".