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Record W4413837511 · doi:10.1111/vop.70067

Ocular Abnormalities in 127 Cats Presented for Companion Animal Eye Registry (<scp>CAER</scp>) Examination in the United States and Canada

2025· article· en· W4413837511 on OpenAlexaboutno aff
Kathryn A. Diehl

Bibliographic record

VenueVeterinary Ophthalmology · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicConnexins and lens biology
Canadian institutionsnot available
Fundersnot available
KeywordsCATSCompanion animalMedicinePathologyVeterinary medicineInternal medicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.385
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.020
GPT teacher head0.287
Teacher spread0.267 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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