Occurrence of canine cardiac diseases in Bangalore
Bibliographic record
Abstract
Cardiac diseases are very common in dogs. Cardiac diseases further leading to congestive heart failure are a major cause of morbidity and mortality in dogs. One hundred dogs presented to the Small Animal Medicine Unit, Department of Veterinary Medicine, Veterinary Hospital, Veterinary College, Hebbal, Bangalore with the classical clinical signs suggestive of cardiac diseases were screened for cardiac diseases based on detailed clinical examination, electrocardiography, radiography, echocardiography, blood pressure measurement and haemato-biochemical assays. In the present study, Dilated Cardiomyopathy followed by Mitral Valve diseases were the most common cardiac disease in dogs. Labrador Retriever breed was the most commonly affected breed with cardiac disease. Older dogs were found to be more commonly affected with cardiac diseases and the incidence were high in aged dogs (>6 years) followed by adult dogs (3-6 years). Males were found to be more commonly affected than females.
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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 | 0.000 | 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) | 0.002 | 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".