Incidence of disease, injury and death in Thoroughbred foals and yearlings on stud farms in the UK and Ireland
Bibliographic record
Abstract
BACKGROUND: Up-to-date estimates of early-life morbidity and mortality in Thoroughbreds are lacking. METHODS: A birth cohort was established on Thoroughbred stud farms across the UK and Ireland. All veterinary interventions for disease or injury between birth and 18 months of age or leaving the study were recorded. Multilevel Poisson regression models with farm and foal as random effects were fitted to estimate incidence rates. RESULTS: Data were available for 3328 foal-months at risk for 275 foals on seven farms. The overall rates of disease and injury requiring veterinary intervention and mortality were 11.9 cases/100 foal-months at risk (95% confidence interval [CI] 8.6-16.2) and 0.2 cases/100 foal-months at risk (95% CI 0.1-0.4), respectively. Almost half (n = 133/273, 49%, 95% CI 43-55) of the live-born cohort required veterinary intervention for musculoskeletal disease or injury, equating to 5.8 cases/100 foal-months at risk (95% CI 4.1-8.2), predominantly reported as developmental orthopaedic disease (DOD). LIMITATIONS: Convenience sampling of participants may affect the generalisability of the findings. CONCLUSIONS: Rates of musculoskeletal disease and injury, in particular DOD, on Thoroughbred stud farms were high. Further work to identify modifiable risk factors and further understanding of the economic impact of these conditions and long-term consequences for musculoskeletal health and performance is required.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".