Mortality in over 350,000 Insured Swedish dogs from 1995–2000: I. Breed-, Gender-, Age- and Cause-specific Rates
Bibliographic record
Abstract
This study presents data on over 350,000 insured Swedish dogs up to 10 years of age contributing to over one million dog-years at risk (DYAR) during 1995-2000. A total of 43,172 dogs died or were euthanised and of these 72% had a claim with a diagnosis for the cause of death. The overall total mortality was 393 deaths per 10,000 DYAR. Mortality rates are calculated for the 10 most common breeds, 10 breeds with high mortality and a group including all other breeds, crudely and for general causes of death. Proportional mortality is presented for several classifications. Five general causes accounted for 62% of the deaths with a diagnosis (i.e. tumour (18%), trauma (17%), locomotor (13%), heart (8%) and neurological (6%)). Mortality rates for the five most common diagnoses within the general causes of death are presented. These detailed statistics on mortality can be used in breed-specific strategies as well as for general health promotion programs. Further details on survival and relative risk by breed and age are presented in the companion paper (Egenvall et al. 2005).
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".