Prevalence and risk factors for mast cell tumours in dogs in England
Bibliographic record
Abstract
BACKGROUND: Mast cell tumour (MCT) appears to be a frequent tumour type in dogs, though there is little published in relation to its frequency in dogs in the UK. The current study aimed to investigate prevalence and risk factors for MCTs in dogs attending English primary-care veterinary practices. METHODS: Electronic patient records from practices participating in the VetCompass animal surveillance project between July 2007 and June 2013 were searched for MCT diagnosis. Various search terms and standard diagnostic terms (VeNom codes) identified records containing MCT diagnoses, which were evaluated against clinical criteria for inclusion to the study. MCT prevalence for the entire dataset and specific breed types were calculated. Descriptive statistics characterised MCT cases and multivariable logistic regression methods evaluated risk factors for association with MCT (P < 0.05). RESULTS: Within a population of 168,636 dogs, 453 had MCT, yielding a prevalence of 0.27% (95% confidence interval (CI) 0.24% - 0.29%). The highest breed type specific prevalences were for the Boxer at 1.95% (95% CI 1.40% - 2.51%), Golden Retriever at 1.39% (0.98% - 1.81%) and Weimaraner at 0.85% (95% CI 0.17% to 1.53%). Age, insurance status, neuter status, weight and breed type were associated with MCT diagnosis. Of dogs of specific breed type, the Boxer, Pug and Staffordshire Bull Terrier showed greater odds of MCT diagnosis compared with crossbred dogs. Conversely, the German Shepherd Dog, Border Collie, West Highland White Terrier, Springer Spaniel and Cocker Spaniel had reduced odds of MCT diagnosis compared with crossbred dogs. No association was found between MCT diagnosis and sex. CLINICAL SIGNIFICANCE: This study highlights a clinically significant prevalence of MCT and identifies specific breed types with predisposition to MCT, potentially aiding veterinarian awareness and facilitating diagnosis.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".