Occurrence and distribution of canine cutaneous mast cell tumour characteristics among predisposed breeds
Bibliographic record
Abstract
INTRODUCTION: Breed predisposition to cutaneous mast cell tumours (MCT) in a population of dogs in Poland affected by various skin tumours was assessed, and the distribution of MCT characteristics such as histological grading, sex, age, and location, in predisposed breeds was evaluated. MATERIAL AND METHODS: The retrospective epidemiological study included 550 dogs affected by cutaneous MCTs with a reference group of 2,557 dogs diagnosed with other skin tumours. RESULTS: A univariable logistic regression analysis was performed to determine the odds ratios (ORs) with 95% confidence intervals. The risk of high-grade MCTs was the highest for Shar-Peis (OR: 26.394) and American Staffordshire Terriers (OR: 2.897). Boxers (OR: 6.619), Labrador Retrievers (OR: 2.630), French Bulldogs (OR: 2.050), Golden Retrievers (OR: 1.949), and American Staffordshire Terriers (OR: 2.592) were mainly affected by low-grade MCTs. The high risk of MCT was calculated to be at the age of 4-6 years for Labrador Retrievers (OR: 2.686) and 7-10 years for Boxers (OR: 2.956) and French Bulldogs (OR: 9.429). MCTs were significantly more often located on the trunk in French Bulldogs (OR: 4.680), American Staffordshire Terriers (OR: 2.520), and Labrador Retrievers (OR: 1.948). There was no statistically significant correlation between gender and the occurrence of MCTs in the breeds. CONCLUSIONS: The breed-predicated differences in the clinical course of MCTs suggest a genetic background for the tumours.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".