Gender, age, breed and distribution of morbidity and mortality in insured dogs in Sweden during 1995 and 1996
Bibliographic record
Abstract
More than 200,000 dogs insured by one Swedish company at the beginning of either 1995 or 1996 were included in a retrospective, cross-sectional study. They could be covered for veterinary care at any age, but were eligible for life insurance only up to 10 years of age. Accessions for veterinary care that exceeded the deductible cost were used to calculate the risk of morbidity. The morbidity and mortality data have been stratified by gender, age, breed, location and human population density. In each year, 13 per cent of the dogs experienced at least one veterinary care event and the mortality risk was 3.0 per cent. The risk of morbidity varied with age, gender, breed, and location. The risk of mortality increased principally with age. It was possible to derive population-based risks of morbidity and mortality from these insurance data.
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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".