Diagnosis and Incidence of Canine Parvovirus Gastroenteritis
Bibliographic record
Abstract
The present study conducted to carry out incidence of canine parvovirus gastroenteritis at Veterinary Clinical Complex, College of Veterinary Science and Animal Husbandry, Kamdhenu University, Anand, Gujarat, India. A total of 1540 dogs were presented at the Veterinary Clinical Complex, Anand during the study period from October 2021 to February 2022.Faecal samples were for confirmation of canine parvovirus gastroenteritis using the polymerase chain reaction (PCR). Out of 1540 dogs, 145 (9.42%) dogs were found positive for Canine Parvo Virus (CPV) by PCR. The age-wise incidence was the highest 66.20 % (96/145) in 0-3 months followed by 22.06 % (32/145) in 4-6 months, 8.96% (13/145) in 7-12 months and 2.76 % (4/145) in >12 months of age. The breed-wise incidence was the highest 51.03% (74/145) in non-descript breeds followed by Labrador 18.62% (27/15), German shepherd 8.96% (13/145), Doberman7.59% (11/145), Pomeranian 5.52% (8/145), Rottweiler 2.76% (4/145), Pug 2.76% (4/145), and Golden Retriever, Lhasa Apso, Beagle and Mongrel 0.86% (1/145) each. The sex-wise incidence was higher 62.07% (90/145) in males as compared to females 37.93% (55/145). The vaccination status wise incidence was the highest 80.69% (117/145) in unvaccinated dogs followed by vaccinated dogs 17.24% (25/145) and partially vaccinated dogs 2.07% (3/145).
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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 | 0.001 | 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".