Factors related to longevity and mortality of dogs in Italy
Bibliographic record
Abstract
Besides its translational value, an improved understanding of dog longevity and mortality is necessary to guide health management decisions, breed selection, and improve dog welfare. In order to analyse the lifespan of dogs in Italy, identify the most common causes of death, and evaluate possible risk factors, anonymised medical records were collected from 9 veterinary teaching hospitals and 2 public health institutions. Data regarding breed, sex, neuter status, age, diagnosis, and mechanism of death were retrieved. Cause of death (COD) was classified by pathophysiologic process (PP) and organ system (OS). Of the 4957 dogs that died between 2004 and 2020 included in the study, 2920 (59.0%) were purebred, 2293 (46.2%) were female, 3005 (60.6%) were intact, 2883 (58.2%) were euthanised. Overall median longevity was 10.0 years. Median longevity was significantly longer for crossbreds, females, neutered dogs, and small-sized breeds. The breeds with the highest median age at death were the Yorkshire terrier, English cocker spaniel, West Highland white terrier, Italian volpino, and Shih Tzu, whilst the American bulldog, English bulldog, American pit bull terrier, Bernese mountain dog and the Maremma and the Abruzzes sheepdog had the lowest median age at death. The most frequent COD by PP was neoplasia (34.0%), which occurred more frequently in large breeds, namely German shepherd, Labrador retriever and Boxer. Degenerative diseases mostly affected small-sized dogs like Miniature pinscher and Dachshund. Regarding the OS involved, diseases of the renal/urinary system were most frequently responsible for COD (15.0%), prevalently degenerative and inflammatory/infectious. Substantial variation in median longevity according to causes of death by PP and OS was observed. These data are relevant for breeders, veterinary practitioners, and owners, to assist breed selection, facilitate early diagnosis, guide choice when purchasing a purebred dog and making health management decisions, and ultimately improve dog welfare.
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How this classification was reachedexpand
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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".