Incidence of Ocular Affections in Dogs with Special Reference to Pigmentary Keratitis
Bibliographic record
Abstract
Background: The aim of current study was to record the Incidence of ocular affections in dogs with special reference to pigmentary keratitis. Methods: Total 6,863 animals were registered at Veterinary Clinical Complex (VCC), Co.V.Sc Jabalpur during a period of six months (May to October, 2024). Out of these animals 5,148 were of dogs, in which 300 (5.82%) dogs were suffered from various ocular affections. Pigmentary keratitis was recorded in 42 (0.81%) dogs after clinical examination. Result: Among the various ocular affections cataracts being the most common at 20.0%, followed by pigmentary keratitis, corneal ulcers, conjunctivitis, corneal edema, eyelid masses and other ocular affections were observed in decreasing order. Labrador Retriever was mostly affected among all breeds representing 25.33% of cases, with male dogs showing a higher distribution at 61.33% compared to females at 38.66%. The age group of 3 to 7 years (36.33%) suffered the most. The incidence of pigmentary keratitis in the study was 14.0%, cases of severe pigmentary keratitis (52.63%), with diffuse pigmentation pattern presented the most, Pug being the most affected breed, accounting for 69.04% of cases. Male dogs were more commonly affected than females, with a distribution of 66.66% and 33.33% respectively. Dogs aged between 3 to 7 years were the most affected, comprising 42.85% of the cases.
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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".