Case-control study of risk factors for pasture-and endocrinopathy-associated laminitis in North American horses
Bibliographic record
Abstract
OBJECTIVE To investigate risk factors for the development of pasture- and endocrinopathy-associated laminitis (PEAL) in horses and ponies in North America. DESIGN Case-control study. ANIMALS 199 horses with incident cases of PEAL and 351 horses from 2 control populations (healthy horses [n = 198] and horses with lameness not caused by laminitis [153]) that were evaluated in North America between January 2012 and December 2015 by veterinarian members of the American Association of Equine Practitioners. PROCEDURES North American members of the American Association of Equine Practitioners were contacted to participate in the study, and participating veterinarians provided historical data on incident cases of PEAL, each matched with a healthy control and a lameness control. Conditional logistic regression analysis was used to compare data on PEAL-affected horses with data on horses from each set of controls. RESULTS Horses with an obese body condition (ie, body condition score ≥ 7), generalized or regional adiposity (alone or in combination), preexisting endocrinopathy, or recent (within 30 days) glucocorticoid administration had increased odds of developing PEAL, compared with horses that did not have these findings. CONCLUSIONS AND CLINICAL RELEVANCE The present study identified several risk factors for PEAL that may assist not only in managing and preventing this form of laminitis, but also in guiding future research into its pathogenesis.
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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 | 0.001 | 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".