Risk factors for equine glandular and squamous gastric disease in show jumping Warmbloods
Bibliographic record
Abstract
BACKGROUND: Prevalence of, and risk factors for, equine squamous gastric disease (ESGD) are well established. Limited data exists on risk factors for equine glandular gastric disease (EGGD). OBJECTIVES: To identify management factors associated with EGGD in show jumping Warmbloods in training. A secondary objective was to identify management factors associated with ESGD. STUDY DESIGN: Cross-sectional. METHODS: Gastroscopies were performed in horses following a 12-16 h fast. Management questionnaires were collected for each horse. Risk factors were determined using multivariable logistic regression modelling. RESULTS: Eighty-three horses were included in the final analysis. Exercising ≥6 days per week increased the odds of EGGD grade ≥1/4 (odds ratio [OR] = 3.5; 95% confidence interval [CI] 1.2-10.7) compared to less frequent exercise. Currently showing increased the risk of EGGD grade ≥2/4 (OR = 10.2; 95% CI, 1.04-100), while competing at the international level decreased the odds of EGGD grade ≥2/4 (OR = 0.11; 95% CI, 0.01-0.97). Exercise intensity increased the odds of grade ≥1/4 ESGD (OR = 2.8; 95% CI, 1.03-7.8) and feeding beet pulp decreased odds (OR = 0.22; 95% CI, 0.07-0.7). Exercise intensity (OR = 3.8; 95% CI, 1.1-12.8) increased the likelihood of grade ≥2/4 ESGD and feeding beet pulp decreased the odds of grade ≥2/4 ESGD (OR = 0.1; 0.02-0.64) respectively. MAIN LIMITATIONS: This study used a convenience sample of horses within a relatively small (approximately 200 km) geographic radius. The sample size was relatively small, particularly within the international competition level group. CONCLUSIONS: Training and feeding strategies and competition level appear to influence the occurrence of EGGD and ESGD. Prospective studies evaluating the impact of training frequency, duration, and intensity on gastric physiology may clarify the role of exercise in gastric disease.
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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 | 0.000 |
| 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) | 0.002 | 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".