Risk factors for equine recurrent uveitis in a population of Appaloosa horses in western Canada
Bibliographic record
Abstract
OBJECTIVE: To characterize clinical manifestations, measure frequency, and evaluate risk factors for equine recurrent uveitis (ERU) in Appaloosa horses in western Canada. ANIMALS: 145 Appaloosa horses. PROCEDURES: Ophthalmic examinations were completed and eyes were classified as having no or mild clinical signs, or moderate, or severe damage from ERU. Clinical signs, age, sex, base coat color, and pattern were recorded. Whole blood and/or mane hair follicles were collected for DNA extraction, and all horses were tested for the leopard complex (LP) spotting pattern allele. Pedigree analysis was completed on affected and unaffected horses, and coefficients of coancestry (CC) and inbreeding (COI) were determined. RESULTS: = 1.19). The fewspot coat pattern was significantly associated with increased risk for ERU compared to horses that were minimally patterned or true solids. The LP/LP genotype was at a significantly greater risk for ERU compared to lp/lp (OR = 19.4) and LP/lp (OR = 6.37). Classification of ERU was greater in the LP/LP genotype compared to LP/lp. Affected horses had an average CC of 0.066, and there was a significant difference in the distribution of CC for affected horses versus the control group (P = .021). One affected horse was the sire or grandsire of nine other affected. CONCLUSIONS: Age, coat pattern, and genetics are major risk factors for the diagnosis and classification of ERU in the Appaloosa.
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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 | 0.001 | 0.001 |
| 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".