Nosological profile of ophthalmological and dental pathology of dogs and cats in the city of Dniprо
Bibliographic record
Abstract
The prevalence of ophthalmological and dental pathology in dogs and cats was determined by analyzing statistical data obtained from veterinary clinics in the city of Dnipro. The most common ophthalmological diseases were conjunctivitis, blepharitis, and keratitis. Corneal ulcers and injuries to the eye and auxiliary apparatus were recorded more often in cats than in dogs, and among oncological diseases, lymphoma of the eye. Cataracts and prolapse of the third eyelid were diagnosed more frequently in dogs. Papillomatous lesions of the oral cavity were recorded only in young dogs. Male individuals were more often affected, and ophthalmological pathology was observed in mestizos and the following breeds: cocker spaniel, poodle, dachshund, shar pei, german shepherd, and labrador. Corneal lesions were often found in brachycephalic dogs. In cats, ophthalmological diseases were mainly diagnosed in mongrels and animals of scottish, persian, british breeds and sphinxes. Of the oncological diseases, eye lymphoma is significantly prevalent in animals, which was recorded with a higher frequency in cats. A significant prevalence of oral diseases in dogs and cats has been confirmed. Of the dental problems, periodontitis, tartar, malocclusion and gingivostomatitis were more frequently diagnosed. In dogs, malocclusion and tumors of the oral cavity were recorded with a higher frequency, and in cats, gingivostomatitis and injuries and fractures of the jaws. Periodontal disease and tartar are the most common dental problems in animals of both species. A predisposition to both ophthalmological and dental pathology is noted in brachycephalics. The conducted statistical analysis provides additional information about the prevalence of ophthalmological and dental pathology in dogs and cats, and the obtained research results may be useful for the diagnosis, treatment and prevention of these diseases.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.015 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".