Comparative epidemiological analysis of tumors of the digestive system in dogs and cats
Bibliographic record
Abstract
Introduction: Gastrointestinal (GI) disorders are a leading reason for veterinary care. Methods: This study analyzed digestive tract tumors in dogs and cats in Portugal using data from the Vet-OncoNet database, focusing on frequency, risk factors, and geographic distribution. Results and discussion: A total of 1,213 cases were included: 617 dogs (50.9%) and 596 cats (49.1%), with a higher proportion of males (54.9%) than females (45.1%). The most affected organs overall were the small intestine (26.5%) and liver/intrahepatic bile ducts (16.7%). In dogs, tumors were mainly located in the liver and bile ducts (25.8%), rectum (19.0%), small intestine (13.8%), and stomach (8.9%). In cats, the small intestine was the primary site (39.6%), followed by liver/bile ducts (7.4%), stomach (7.3%), and colon (3.5%). Lymphoma was the most common tumor type in both species (42.2%), followed by adenocarcinoma (19.0%). Among dogs, mixed breeds, Labrador Retrievers, German Shepherds, and French Bulldogs were most affected. In cats, Common European, mixed-breed, and Norwegian Forest cats predominated. The incidence rate (IR) of digestive tumors was 3.5 times higher in cats than dogs. Male cats had a 1.5 times higher IR than females. Cats also had 16 times higher risk for GI lymphoma and twice the risk for adenocarcinoma compared to dogs. Certain dog breeds, including West Highland White Terrier, Siberian Husky, and Golden Retriever, showed higher tumor incidence. Spatial analysis revealed concentration in urbanized areas, particularly around Porto and Lisbon. Conclusion: These findings highlight notable species-specific differences in digestive tract tumors, suggesting distinct genetic predispositions and possible environmental influences.
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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 | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".