Disease patterns and incidence of immune‐mediated disease in insured Swedish Nova Scotia Duck Tolling Retrievers
Bibliographic record
Abstract
In this study, morbidity in insured Nova Scotia Duck Tolling Retriever (NSDTR) dogs from Sweden was investigated and compared with all other breeds and other retriever breeds. In addition to describing common morbidities in NSDTRs, the hypotheses that NSDTRs are predisposed to lymphoma, immune-mediated rheumatic disease (IMRD) and steroid-responsive meningitis-arteritis (SRMA) were tested. Included in the study were 445,336 dogs; of which, 2890 were NSDTRs that had been covered by veterinary insurance from the Agria Insurance Company (Stockholm, Sweden) at some point during the years 1995-2006. Incidences of various health problems were calculated using the number of veterinary visits as the numerator and the exact time at risk as the denominator. Overall, morbidity was higher in NSDTRs compared with all other breeds, but similar compared with other retriever breeds. The most common causes of veterinary visits in NSDTRs were injuries, gastrointestinal disease and locomotor disorders, with NSDTRs at increased risk of these compared with all other breeds. The incidences for IMRD, SRMA and lymphoma were significantly higher in NSDTRs than in all other dog breeds and all other retriever breeds. The study describes morbidity in NSDTRs, and identifies several disorders to which the breed is predisposed.
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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 | 0.000 | 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".