A Study on Prevalence and Clinico-pathological Findings Associated with Ascites in Dogs
Bibliographic record
Abstract
Canine ascites is a common illness with multiple aetiologies. However, the involvement of various etiological factors is making therapeutic management extremely difficult for the clinicians. The present study was conducted on dogs with distension of abdomen at Veterinary Clinical Complex, College of Veterinary Sciences, Assam Agricultural University, Khanapara, Assam, India, to study the prevalence of the disease with respect to primary organ involvement in the pathogenesis of ascites and hemato-biochemical changes. The blood samples from the dogs were collected for hemato-biochemical analysis. Routine blood smear, fecal smear examination, ultrasonography and echocardiography of all the ascetic dogs were carried out to find out the exact etiology of ascites. Out of the total 7788 dogs assessed throughout the period from August, 2022 to January, 2023, 32 dogs (0.41%) developed ascites. The highest prevalence of ascites 53.12% was recorded in the dogs of above 6 years of age and breed wise Labrador breed (34.37%) of dogs were most commonly affected. Most of the ascites cases were found to be largely due to liver diseases (34.37%); 31.25% were due to congestive heart failure, 21.87% due to infectious causes and 12.50% due to kidney disease. The hemato-biochemical analysis revealed anemia, mild leukocytosis, hypoproteinaemia, hypoalbuminemia, hyperbilirubinemia, increased alanine aminotransferase (ALT), aspertate aminotransferase(AST), lactate dehydrogenase(LDH), blood urea nitrogen (BUN) and creatinine level in the ascitic dogs.
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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".