Morbidity of insured Swedish cats between 2011 and 2016: Comparing disease risk in domestic crosses and purebreds
Bibliographic record
Abstract
BACKGROUND: Sources of population-based cat health information are scarce. The objective of this study was to determine disease frequency in cats using pet insurance data to inform health promotion efforts. METHODS: A descriptive analysis of cats insured with Agria Pet Insurance in Sweden (2011-2016) was performed. Incidence rates of broad disease categories were calculated based on veterinary care events and an exact denominator consisting of cat-years-at-risk. Rate ratios were calculated, comparing domestic crosses to all purebreds and specific purebreds to all other purebreds combined. RESULTS: The study included over 1.6 million cat-years-at-risk (78.5% were domestic crosses), 18 breeds and 24 disease categories. The most common disease categories causing morbidity in purebreds were digestive, whole body, injury, urinary lower, skin and female reproduction. Purebreds had the highest relative risk (compared to domestics crosses) in the female reproduction, heart, operation complication, respiratory lower and immunological disease categories. LIMITATIONS: There are typical limitations of secondary data, but they do not negate the overall value of such a large dataset. CONCLUSION: This study demonstrates how pet insurance data can be used to find breed-specific differences in the incidence of various disease categories in cats. This may be of importance for breeders, cat owners, veterinarians and researchers.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".