Prevalence Study (2017-2020) of Heart Failure among Dogs of Hyderabad, Telangana State
Bibliographic record
Abstract
Background: Heart failure that is caused by various cardiac diseases like valvular disease, dilated cardiomyopathy and pericardial diseases are progressive and chronic in nature, but occur as acute form wherein the clinician has to respond quickly both in its diagnosis and treatment. Methods: The present study that was carried out to assess the prevalence of heart failure has included 309 dogs of various breed, gender and age. Clinical evaluation was followed by electrocardiography using smart phone-based ECG, thoracic radiography and 2d-echocardiography to diagnose the various causes of heart failure. Result: All of these cases were exhibiting similar manifestations like generalized weakness, poor physical activity, respiratory distress, dyspnoea at rest, cough and lack of sleep, that were suggestive of heart failure. ECG, thoracic radiography and echocardiographic evaluation revealed 146/309 cases as suffering with various cardiac diseases viz., valvular diseases, dilated cardiomyopathy and pericardial diseases that resulted in heart failure. However, the remaining 163/309 dogs were diagnosed with respiratory disorders (69), renal insufficiency (57), hepatic insufficiency (26), pyometra (07) and anemia (04). The overall prevalence of heart failure was observed to be 1.55% (146) among the total dogs (9369) presented with systemic diseases, while it was 47.25% (146) among dogs (309) which were exhibiting clinical manifestations suggestive of heart failure. Similarly, the heart failure was recorded as highest prevalent among Labrador and lowest among Daschund. Dogs aged between 10-14 years are more vulnerable for these cardiac diseases when compared to various other age group individuals and males are more at risk.
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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 | 0.000 |
| 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) | 0.002 | 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".