Studies on Clinico-Etio-Epidemiology of Vomition in Dogs
Bibliographic record
Abstract
The present study of etio-epidemiology of vomition in dogs was carried out amongst the dogs presented at Veterinary Clinical Complex, AAU, Anand. A total of 50 cases of vomiting dogs were selected for this study. Investigation of etiological factors associated with vomition revealed that 50.00% vomiting cases were due to dietary abnormalities, 24.00% due to Parvo viral infection, 10.00% due to parasitic infestation, 8.00% renal disorders, 6.00% due to hepatic disorders, and 2.00% due to pyometra. The epidemiological parameters like sex, breed, and age were recorded. During the sex-wise incidence of vomition was found more in male (64.00 %) than in female (36.00 %) dogs. The breed-wise incidence of vomition was recorded highest (32.00 %)in Mongrel, followed by Labrador Retriever (28.00 %), Spitz (12.00 %), Doberman Pinscher (8.00 %), German Shepherd (10.00 %), Great Dane 4%, Beagle (2.00 %), Lasa Aphso (2.00 %), and Pug (2.00 %) . The age-wise incidence of vomition was recorded higher (58.00%) in 0-6 months age group, followed by (24%) in 7 months to 3 years age group and (18%) in 3-8 years age group. Clinical examination revealed dehydration, dullness, congested to anemic mucus membranes, inappetence to anorexia, tachycardia, melaena, haematemesis, diarrhea, and clear lungs. On abdominal palpation, there was mild pain in dogs affected with renal and hepatic disorders.
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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".