Choroidal melanocytic tumors in dogs: A retrospective study
Bibliographic record
Abstract
OBJECTIVE: To describe clinical features of dogs diagnosed with choroidal melanocytic tumors at a veterinary teaching hospital. ANIMALS STUDIED: Retrospective case series of 13 dogs (14 melanocytic tumors) with choroidal melanocytic tumors. PROCEDURES: Medical records of dogs evaluated by the Cornell University ophthalmology service with a clinical diagnosis of a choroidal melanocytic tumor between 2008 and 2020 were reviewed. A choroidal melanocytic tumor was clinically defined as any well-demarcated, raised pigmented choroidal lesion underlying the retina. Histopathology results were consulted when available. Signalment and clinical features were recorded, including fundoscopic location, histopathologic findings, treatment, and outcome. RESULTS: Choroidal melanocytic tumors were identified in 14 eyes of 13 dogs. The mean (±standard deviation) of dogs was 8.6 (±3.5) years. Seven different breeds were represented, with Labrador/Labrador mix being the most common. Ten of the 14 melanocytic tumors were diagnosed incidentally. Four dogs presented for vision loss and ocular discomfort, and diagnosis was made on histopathology examination following enucleation. Pulmonary metastasis was suspected in one dog with an incidentally found choroidal melanocytic tumor OD. Fundoscopic location was available for 8 melanocytic tumors, with 6 (75%) located in the tapetal fundus. Histopathologic diagnosis was melanocytoma for 3 and malignant melanoma in 1 globe, with optic nerve head invasion in 50%. Local recurrence was not seen in any orbit following enucleation. CONCLUSION: Choroidal melanocytic tumors are uncommon in dogs. Metastasis appears to be rare and was only suspected in one dog, but intraocular tumor growth can lead to retinal detachment, glaucoma, and necessitate enucleation.
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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 | 0.000 | 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) | 0.001 | 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".