Mortality of Life-Insured Swedish Cats during 1999–2006: Age, Breed, Sex, and Diagnosis
Bibliographic record
Abstract
BACKGROUND: A cat life insurance database can potentially be used to study feline mortality. HYPOTHESIS: The aim was to describe patterns of mortality in life-insured Swedish cats. Cats: All cats (<13 years of age) with life insurance during the period 1999-2006 were included. METHODS: Age-standardized mortality rates (MR) were calculated with respect to sex (males and females), age, breed, and diagnosis. Survival to various ages is presented by time period and breed. RESULTS: The total number of cats insured was 49,450 and the number of cat-years at risk (CYAR) was 142,049. During the period, 6,491 cats died and of these 4,591 cats (71%) had a diagnosis, ie, were claimed for life insurance. The average annual MR was 462 deaths per 10,000 CYAR (95% confidence interval, 431-493). Sex-specific rates did not differ significantly. The overall mortality of the Persian and the Siamese groups was higher than that of several other breeds. Overall and breed-specific (for most breeds) survival increased with time when analyzed by 2-year periods. The 6 most common diagnostic categories (ignoring cats recorded as dead with no diagnosis) were urinary, traumatic, neoplastic, infectious, cardiovascular, and gastrointestinal. The MR within diagnostic categories varied by age and breed. CONCLUSIONS AND CLINICAL IMPORTANCE: In this mainly purebred, insured cat population, the overall mortality varied with age and breed but not with sex. The increase in survival over time is likely a reflection of willingness to keep pet cats longer and increased access to and sophistication of veterinary care.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".