Clinicopathologic profiles of canine ocular melanosis: A comparative study between cairn terriers and non‐cairn terriers
Bibliographic record
Abstract
OBJECTIVES: To identify canine breeds at risk for ocular melanosis and to compare the clinical and histologic features between affected Cairn Terriers (CTs) and non-Cairn Terriers (NCTs). DESIGN: Relative risk (RR) analysis and retrospective cohort study of dogs histologically diagnosed with ocular melanosis. PROCEDURES: The COPLOW archive was searched for globe submissions diagnosed with ocular melanosis. Six hundred fifty globes were included, and RR analysis was performed to identify at-risk NCT breeds. A cohort of 360 CT and NCT globes diagnosed from 2013 to 2023 were included in the retrospective cohort study. Clinical data were collected from submission forms, medical records, and follow-up surveys. One hundred fifty-seven submissions underwent masked histologic review. Immunohistochemical staining for CD204 was performed to determine the predominance of melanophages in affected uvea from five NCTs. RESULTS: At-risk NCT breeds included the Boxer, Labrador Retriever, and French Bulldog. Glaucoma was the reported reason for enucleation in 79.4% of submissions. At enucleation, clinical features less prevalent in NCTs than CTs included pigmentary abnormalities in the contralateral eye (33.7% vs. 63.1%, p = .0008) and abnormal episcleral/scleral pigmentation in the enucleated globe (25.4% vs. 53.6%, p = .0008). Histologic involvement of the episclera was also less frequent in NCTs than in CTs (39.7% vs. 76.9%, p = .008). Concurrent melanocytic neoplasms arising in melanosis were more common in NCTs (24.4%) than CTs (3.9%). Melanophages were not predominant in any samples evaluated immunohistochemically. CONCLUSIONS: Several popular NCT breeds carry risk for ocular melanosis, and some clinicopathologic disease features may differ from those described in CTs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.002 | 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".