Ocular Abnormalities in 127 Cats Presented for Companion Animal Eye Registry (<scp>CAER</scp>) Examination in the United States and Canada
Bibliographic record
Abstract
STUDY OBJECTIVE: To investigate the prevalence and clinical appearance of ocular abnormalities in a US and Canadian population of cats registered with the Companion Animal Eye Registry (CAER) between 2013 and 2023. METHODS: All complete (131) feline CAER exam forms from 2013 to 2023 in the OFA database, were reviewed. Available pedigrees of cats included in this study were also investigated. The central tendencies and dispersion data were reported. RESULTS: The population consisted of 113 Bengal, six British Shorthair, two each Maine Coon, and Norwegian Forest, and one each Domestic Shorthair, Ragamuffin, Siberian, and Sphynx cats. Four cats had serial CAER examinations performed. Twenty-two (20%) Bengal cats had normal ophthalmic exams, and 91 (80%) had abnormalities noted. Eighty-one (72%) of the Bengal cats had cataract(s) noted, of which the characteristics varied widely. DISCUSSION: Suggestive of a possible hereditary basis, there was a high prevalence of young Bengal cats presented for CAER examinations between 2013 and 20233 that had functionally incidental cataracts that were expected to be essentially nonprogressive. Most of the noted feline cataracts were bilateral, symmetric, punctate or incipient, and nuclear or posterior. Very minimal pedigree information received supported a possible hereditary basis of cataracts among the British Shorthair cats, but was insufficient to confirm this or apply to the entire study population.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".