Bibliographic record
Abstract
Feline coronavirus enteritis is widespread throughout the world and is known to cause disease in both domestic and wild feline species. In some individuals, the viral disease is a consequence of infectious peritonitis. To study the prevalence of feline coronavirus enteritis in the world, a literature analysis was performed using resources such as the Google Scholar website, the scientific portal ResearchGate, the official website of the U.S. government, the National Center for Biotechnology Information, and the international journal Sciencedirect. The epizootiological characteristics of infectious peritonitis in cats were studied taking into account the nosological profile, breeds, age, and seasonality. Outpatient admission records for the years 2022 and 2023 were used. We analyzed 535 cats for infectious diseases during this period. It was found that panleukopenia was diagnosed in 200 cats (37%) during this period, followed by rhinotracheitis (herpesvirus infection) in 137 (25.6%) animals. Calicivirus infection was the third most common. 90 (17%) cats became ill with it. 15 cats fell ill with feline coronavirus enteritis (infectious peritonitis), which is 3%. In the structure of viral diseases of cats coronavirus enteritis is in: Australia - 34-54%, Croatia - 42%, Czech Republic - 63%, Galapagos and Falkland Islands - 0%, France 17%, Germany - 62%, Greece - 10-19%, Italy - 19-51%, Great Britain - 20-65%, USA - 56%, China - 12.7%, Japan - 31-67%, Korea - 7-14%, Malaysia - 70-90%. Coronavirus enteritis in cats is not very common in the nosological profile of infectious diseases in Ukraine and according to our researches it is 3%. Panleukopenia was the first disease in 200 cats (37%), followed by rhinotracheitis (herpesvirus infection) in 137 (25.6%) animals. Calicivirus infection was the third most common. 90 (17%) cats had this infection. We found that 7 breeds of cats (British Shorthair, Sphynx, Scottish Fold, Devon Rex, Metis, Bengal, Maine Coon) suffered from infectious peritonitis. At the same time, cats of the Maine Coon and Metis breeds got sick the most. It was found that the peak of clinical manifestation of the disease is observed in October and November. The disease is difficult to treat and has a high mortality rate of 37.5%. The disease was more severe in cats with dry form. They were twice as likely to die as cats with a wet form. Cats between 3 and 6 months of age were most affected by infectious peritonitis, accounting for 33.34% of the age structure. The disease was also more frequent in cats aged 9 months to 2 years
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".