Risk factors for insidious uveitis in the Knabstrupper breed
Bibliographic record
Abstract
BACKGROUND: Equine recurrent uveitis (ERU) is the leading cause of blindness for horses; previous research implicated the leopard complex spotting allele (LP) as a genetic risk factor for insidious uveitis in the Appaloosa. There is limited information about risk in the Knabstrupper. OBJECTIVE: To evaluate clinical manifestations, disease frequency and potential risk factors for ERU in Knabstrupper horses. STUDY DESIGN: Cross-sectional study. METHODS: Ocular examinations were performed on 116 horses, and based on identified anomalies, horses were classified as suspect, ERU-affected or having no clinical signs. Microagglutination testing (MAT) of serum assessed exposure to Leptospira spp. Clinical signs, age, sex, base colour, coat pattern, LP and PATN1 genotypes, percent white at birth, progressive roaning and Leptospira were assessed as risk factors using multivariable exact logistic regression, accounting for clustering at the barn level. Additionally, a pedigree analysis was performed (n = 20 cases and 21 controls), and coefficients of coancestry (CC) and inbreeding were calculated. RESULTS: Prevalence of insidious uveitis in this sample of Knabstruppers was 20.7%. Similar to findings for Appaloosas, LP homozygotes had higher odds of uveitis compared with true solid (N/N) horses (LP/LP OR = 7.64, 95% CI [0.8 to +INF], p = 0.04) and age was also identified as a risk factor. After accounting for LP, the 16-20 age group had higher odds compared with the youngest group (OR = 13.36, 95% CI [1.4-213.4], p = 0.009). The distributions of average CC were significantly different between cases and controls (p = 0.01). MAIN LIMITATIONS: A relatively small sample size decreased the power for detecting additional associations. The progressive nature of insidious uveitis may have prevented identification of younger affected horses. CONCLUSIONS: Our data support genotyping for LP to assess risk of ERU in Knabstruppers. Additional studies are necessary to develop more robust risk models across LP breeds for earlier detection and improved clinical management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".