Descriptive Study of Medication Usage and Occurrence of Disease and Injury During Gestation in Thoroughbred Broodmares
Bibliographic record
Abstract
The study aimed to (1) describe the use of reproductive therapeutics; (2) estimate the incidence of disease and injury; and (3) describe non-reproductive medications administered during pregnancy in Thoroughbred broodmares. A prospective birth cohort was established on seven farms across the UK and Ireland. Details of dams' signalment, breeding history, reproductive management during the breeding season(s) and veterinary-attended episodes of illness or injury and medication usage during gestation were retrieved retrospectively for 275 pregnancies in 235 mares over two breeding seasons. Results are reported at pregnancy-level of mares with data available. Preoestrus medications, ovulatory agents and post-covering treatments were administered to 55% (n = 85/155, 95% Confidence interval (CI) 47-62), 64% (n = 101/157, 95% CI 57-71) and 73% (n = 109/150, 95% CI 65-79) of mares respectively. Antibiotics were utilized in 69% (n = 75/109, 95% CI 60-77) of post-covering treatments. Of mares with no visible fluid on post-covering ultrasound, 37% (n = 24/65, 95% CI 26-49) still received treatment. Thirty-four percent (n = 70/203, 95% CI 28-41) of mares suffered at least one veterinary-attended episode of disease or injury, with conditions affecting the musculoskeletal system (23%, n = 46/203, 95%CI 17-29) and placentitis (5%, n = 10/203, 95% CI 3-9) most prevalent. Forty-seven percent (n = 95/203, 95% CI 40-54) of mares received at least one non-reproductive medication during gestation, antibiotics (25%, n = 51/203, 95% CI 20-31) and non-steroidal anti-inflammatory drugs (23%, n = 47/203, 95% CI 18-29) being most frequently prescribed. Post-covering treatments often included antibiotics and were sometimes given in the absence of fluid, highlighting a need to further understand therapeutic rationale. Disease occurrence and medication usage during gestation were frequent and warrant additional investigation.
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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 | 0.000 | 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".