Incidence and risk factors for insulinoma diagnosed in dogs under primary veterinary care in the UK
Bibliographic record
Abstract
Insulinoma is the most common pancreatic tumor diagnosed in dogs. This study aimed to report incidence risk, breed predispositions and other demographic risk factors for insulinoma diagnosed in dogs under primary veterinary care in the UK. The VetCompass Program supports research on anonymized electronic health records (EHRs) from dogs under UK veterinary care. This study included all VetCompass EHRs from dogs under primary veterinary care during 2019. Multivariable logistic regression analysis was used to evaluate demographic risk factors for insulinoma diagnosis. Of 2,250,741 study dogs, 278 were confirmed as insulinoma cases at any date. The estimated 2019 incidence risk was 0.003% (95% CI 0.002-0.004%). Compared to crossbreeds, predisposed breeds included Dogue de Bordeaux, German Pointer, Flat Coated Retriever, Boxer and West Highland White Terrier. The Labrador Retriever showed decreased odds for insulinoma diagnosis. Additionally, being a terrier breed and being a breed predisposed to other endocrine cancers were associated with increased odds for insulinoma diagnosis. Other risk factors associated with increased odds for insulinoma diagnosis included being female neutered, being 9 - <15 years of age, having an adult median bodyweight of 20 - <30 kg and having a bodyweight above the median for the sex/breed. This is the first study to report the epidemiology of canine insulinoma in dogs under primary veterinary care, resulting in crucial leads for further research in the epidemiology and etiology of canine insulinoma and possible links of canine insulinoma with other canine endocrine cancers. Additionally, the results can aid veterinarians to identify dogs at greater risk of insulinoma.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".