Clinical epidemiology of congenital heart diseases in dogs: prevalence, popularity and volatility throughout twenty years of clinical practice
Bibliographic record
Abstract
Abstract The epidemiology of Congenital Heart Diseases (CHDs) has changed over the past twenty years. We evaluated the prevalence of CHDs in the population of dogs recruited in a single referral center (RC); compared the epidemiological features of CHDs in screened breeds (Boxers) versus nonscreened (French and English Bulldogs and German Shepherds), investigated the association of breeds with the prevalence of CHDs, determined the popularity and volatility of breeds over a 20-year period; and analysed the trends of the most popular breeds in the overall population of new-born dogs registered in the Italian Kennel Club from 1st January 1997 to 31st December 2017. This was a retrospective observational study, the cardiological database of the RC was analysed, and 1,779 clinical records fulfilled the inclusion criteria. Descriptive statistics and frequencies regarding the most representative breeds and CHDs were generated. A logistic regression model was used to analyse the trends of the most common CHDs found in single breeds (French Bulldog, English Bulldog, Boxer, and German Shepherd), and in groups of breeds (brachycephalic breeds and the most represented large breeds). The relationships between the breed popularity and the presence of CHDs was studied. The most common CHDs were Pulmonic Stenosis (34,1%), Patent Ductus Arteriosus (26,4%), Subaortic Stenosis (14,6%), Ventricular Septal Defect (4,8%), Aortic Stenosis (4,7%), Tricuspid Dysplasia (3,4%), Atrial Septal Defect (1,9%), Double Chamber Right ventricle (1,8%), Mitral Dysplasia (1,6%), and reverse Patent Ductus Arteriosus (0,7%). The most represented pure breeds were Boxer (19,4%), German Shepherd (9,4%), French Bulldog (6,2%), English Bulldog (4,9%), Maltese (3,7%), Newfoundland (3,1%), Rottweiler (3,1%), Golden Retriever (3,0%), Chihuahua (2,8%), Poodle (2,5%), Cavalier King Charles Spaniel (2,2%), American Staffordshire Terrier (2,1%), Labrador Retriever (2,3%), Dobermann (2,1%), Miniature Pinscher (2,0%), Cocker Spaniel (2,0%), Yorkshire Terrier (1,7%), Dogue de Bordeaux (1,6%), Dachshund (1,6%), and Bull Terrier (1,5%). Chihuahuas, American Staffordshire Terriers, Border Collies, French Bulldogs, and Cavalier King Charles Spaniel were the most appreciated small and medium breeds, all of which showed a high value of volatility. In conclusion, this study found evidence for the value of the screening program implemented in Boxers, which decreased the prevalence of Subaortic Stenosis and Pulmonic Stenosis. However, fashions and trends influence dog owners’ choices more than the worries of health problems frequently found in a breed. Effective breeding programs are needed in order to control the diffusion of CHDs without impoverishing the genetic pool; in addition, dog owners should be educated, and the breeders supported by a network of veterinary cardiology centers.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".