Prevalence of Congenital Heart Diseases in Dogs in Tehran, Iran: A Retrospective Study From 2013 to 2023
Bibliographic record
Abstract
Congenital heart disease (CHD) is a major health issue in dogs, contributing to both morbidity and mortality. This retrospective study reviews the epidemiological features and prevalence of CHD in dogs visiting veterinary facilities in Tehran, Iran, over the last 10 years. Medical records were analyzed for 4033 canines that underwent comprehensive cardiac examinations, including echocardiography, between January 2013 and October 2023. In this study, 88 cases of CHD were detected, and an overall prevalence of 2.18% was determined. A significant difference was noted between mixed-breed dogs (8.65%) and purebred dogs (1.63%). Pulmonary stenosis (PS) is the most commonly diagnosed CHD, followed by subaortic stenosis (SAS) and patent ductus arteriosus (PDA). CHD prevalence correlated strongly with age and gender; in particular, females and older dogs were more likely to suffer from specific CHDs. CHD is most often diagnosed without symptoms, highlighting the importance of regular screenings and careful auscultation for early detection. Future research must focus on identifying the genetic factors that make dogs more susceptible to CHDs and developing more effective methods for diagnosing and treating these conditions in canine populations. This study does not represent the general dog population in the region or the country but provides researchers with valuable insights into the epidemiology of CHD in dogs referred to veterinary hospitals in Tehran, Iran, underlining the importance of monitoring and focused therapies to improve their health and general well-being.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".