Epidemiology of canine parvovirus infection in and around Pantnagar, Uttarakhand: A retrospective study
Bibliographic record
Abstract
In India, Canine Parvovirus Infection is an endemic viral disease-causing severe gastroenteritis and significant numbers of deaths in puppies, even in vaccinated populations. A retrospective study was conducted between June, 2021 and June, 2022 in and around Pantnagar, Uttarakhand, in which cases of gastro-enteritis were screened for canine parvovirus infection. A total of 258 cases out of 627 cases presented for gastro-enteritis were found to be positive for canine parvovirus based on Rapid Antigen Tests and Polymerase Chain Reaction with a prevalence rate of 41.15%. Data associated with factors such as age, breed, sex, season, immunisation and relocation stress were recorded. Mongrels were found to be the most affected among various breeds, with a prevalence rate of 51.16%, followed by the exotic breed Labrador retriever (9.68%). Males (63.57%) were more found to be affected more than females (34.43%). As for age, prevalence was higher in the age group of 3-6 months (43.40%), followed by less than 3 months of age (31.40%) respectively. Considering other risk factors such as season, vaccination status and relocation stress, prevalence was seen to be higher in to be higher comparatively in spring (33.33%) and winter (29.07%); also, higher prevalence in non-vaccinated (63.13%) and about 25.19% of the animals which were relocated recently were found to be infected with canine parvovirus.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".