Novel risk factors associated with fatal musculoskeletal injury in Thoroughbreds in North American racing (2009–2023)
Bibliographic record
Abstract
BACKGROUND: The Equine Injury Database (EID) is a census-level record of Thoroughbred racing in North America, currently recording 95.6% of all race starts in 2023, along with partial training and veterinary histories of each horse. OBJECTIVES: To identify horse-, race- and track-level risk factors associated with race-related fatal musculoskeletal injury (MSI) of Thoroughbred racehorses in North America. STUDY DESIGN: Retrospective cohort study. METHODS: The study cohort included all race starts made by horses born after 31 December 2006, at tracks that fully report to the EID and consisted of 3,851,659 race starts made by 250,840 Thoroughbred racehorses (median [IQR] starts per horse 11 [5-22]) at 115 racetracks in the USA and Canada between 2009 and 2023, inclusive. Ninety-seven potential risk factors were investigated using univariable and multivariable logistic regression modelling. RESULTS: Exactly 5733 fatal MSIs were recorded, an incidence of 1.49 fatal MSIs per 1000 starts. Twenty risk factors had statistically significant associations with increased or decreased odds of fatal MSI. Previously unidentified risk factors included claiming race-related variables and void claim rules (VCR). Horses racing as claimers were at increased odds compared with those who were not (odds ratio 1.31, 95% confidence interval 1.19-1.45, p < 0.001 for the lowest claim prices). Starts in races with the strictest VCR were at reduced odds compared with starts in races with no VCR (OR 0.76 [0.67-0.85], p < 0.001). MAIN LIMITATIONS: Availability of new data sources increased substantially during the 15-year time period of the study, meaning some new risk factors are limited in scope compared with others. CONCLUSIONS: Thoughtful integration of new data sources with race-level data can lead to new insights into risk factors for deleterious outcomes affecting racehorses. Results can inform ongoing efforts to mitigate the risk of fatal MSI, through direct regulatory intervention and through building a risk profile based on individual history and track-level factors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 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".