Risk factors associated with fatal injuries in Thoroughbred racehorses competing in flat racing in the United States and Canada
Bibliographic record
Abstract
OBJECTIVE To identify risk factors associated with fatal injuries in Thoroughbred racehorses in the United States and Canada. DESIGN Retrospective study. ANIMALS 1,891,483 race starts by 154,527 Thoroughbred racehorses at 89 racetracks in the United States and Canada from 2009 to 2013. PROCEDURES Data were extracted from the Equine Injury Database, which contained information for 93.9% of all official flat racing events in the United States and Canada during the 5-year observation period. Forty-four possible risk factors were evaluated by univariate then multivariable logistic regression to identify those that were significantly associated with fatal injury (death or euthanasia of a horse within 3 days after sustaining an injury during a race). RESULTS 3,572 race starts ended with a fatal injury, resulting in a period incidence rate of 1.9 fatal injuries/1,000 race starts. Twenty-two risk factors were significantly associated with fatal injury. Risk of fatal injury was greater for stallions than for mares and geldings and increased as the number of previous nonfatal injuries and race withdrawals and level of competitiveness (eg, horse's winning percentage and race purse) of the horse or race increased. CONCLUSIONS AND CLINICAL RELEVANCE Results identified several risk factors associated with fatal injuries in Thoroughbred racehorses. This information can be used as a guideline for the identification of racehorses at high risk of sustaining a fatal injury and in the design and implementation of preventative measures to minimize the number of fatal injuries sustained by horses competing in flat racing in the United States and Canada.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".