Mortality of Swedish horses with complete life insurance between 1997 and 2000: variations with sex, age, breed and diagnosis
Bibliographic record
Abstract
The aim of this study was to evaluate the potential usefulness of the database maintained by the Swedish insurance company Agria for providing mortality statistics on Swedish horses. Mortality statistics (incidence rates and survival) were calculated, both crudely and stratified by sex, age, breed, breed group and diagnosis, for the horses with complete life insurance, which covers most health problems. The total mortality was 415 (95 per cent confidence interval [CI] 399 to 432) deaths per 10,000 horse-years at risk, and the diagnostic mortality, including only deaths with an assigned diagnosis, was 370 (95 per cent CI 355 to 386) deaths per 10,000 horse-years at risk. The diagnostic mortality of geldings was 459 (95 per cent CI 431 to 487), of mares 345 (95 per cent CI 322 to 365) and of stallions 214 (95 per cent CI 182 to 247) deaths per 10,000 horse-years at risk. The mortality rates increased with age and differed widely between breeds. Survival analysis showed that the median age at death of the horses enrolled before they were one year of age was 18.8 years. The most common cause of death or euthanasia was joint problems, which were responsible for 140 (95 per cent CI 130 to 149) deaths per 10,000 horse-years at risk. The results of multivariable models developed by using Poisson regression generally agreed well with the crude results.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".