Heart Disease as a Cause of Death in Insured Swedish Dogs Younger Than 10 Years of Age
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Population-based information on disease occurrence is paramount in clinical decision making and in designing preventative measures, but such information is scarce. HYPOTHESIS: The risk of cardiac death is higher in certain breeds and mortality varies by age and sex. DOGS: Dogs that were life insured by an animal insurance company between 1995 and 2002. METHODS: The mortality pattern for heart disease in insured dogs up to 10 years of age was studied. The influences of sex, age, breed, month, and geographic location were investigated by means of incidence rates, proportions, and survival proportions. Cox proportional hazards regression was used to model time to heart disease. RESULTS: 405,376 dogs contributed to a denominator of 1,431,933 dog-years at risk (DYAR) and 3,049 dogs had been assigned a cardiac-related diagnosis as cause of death. The cardiac-related mortality for dogs <10 years of age, was 21.3 deaths per 10,000 DYAR. This mortality in males and females was 27.3 deaths and 15.4 deaths per 10,000 DYAR, respectively. Twelve of 54 breeds had a point estimate above the overall rate. The 3 breeds with the highest point estimates were: Irish Wolfhounds, Cavalier King Charles Spaniels, and Great Danes (rates of 356, 247, and 179 deaths per 10,000 DYAR, respectively). CONCLUSIONS AND CLINICAL IMPORTANCE: Breed, age, and sex affect cardiac mortality in certain breeds of dogs, but no effects of month and geographic location were identified. These findings can assist clinicians in establishing diagnoses, and can assist breeders in defining priorities for preventative measures.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it