Breed Risk of Pyometra in Insured Dogs in Sweden
Bibliographic record
Abstract
Abstract An animal insurance database containing data on over 200,000 dogs was used to study the occurrence of pyometra with respect to breed and age during 1995 and 1996 in Swedish bitches <10 years of age. A total of 1,803 females in 1995 and 1,754 females in 1996 had claims submitted because of pyometra. Thirty breeds with at least 800 bitches insured each year were studied using univariate and multivariate methods. The crude 12-month risk of pyometra for females <10 years of age was 2.0% (95% confidence interval = 1.9-2.1%) in 1995 and 1.9% (1.8-2.0%) in 1996. The occurrence of pyometra differed with age, breed, and geographic location. The risk of developing pyometra was increased (identified using multivariate models) in rough Collies, Rottweilers, Cavalier King Charles Spaniels, Golden Retrievers, Bernese Mountain Dogs, and English Cocker Spaniels compared with baseline (all other breeds, including mixed breed dogs). Breeds with a low risk of developing the disease were Drevers, German Shepherd Dogs, Miniature Dachshunds, Dachshunds (normal size), and Swedish Hounds. Survival rates indicate that on average 23–24% of the bitches in the databases will have experienced pyometra by 10 years of age. In the studied breeds, this proportion ranged between 10 and 54%. Pyometra is a clinically relevant problem in intact bitches, and differences related to breed and age should be taken into account in studies of this disease.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".