Cutaneous tumors in Swiss dogs: retrospective data from the Swiss Canine Cancer Registry, 2008–2013
Bibliographic record
Abstract
Data collected in animal cancer registries comprise extensive and valuable information, even more so when evaluated in context with precise population data. The authors evaluated 11 740 canine skin tumors collected in the Swiss Canine Cancer Registry from 2008-2013, considering data on breed, sex, age, and anatomic locations. Their incidence rate (IR) per 100 000 dogs/year in the Swiss dog population was calculated based on data from the official and mandatory Swiss dog registration database ANIS. The most common tumor types were mast cell tumors (16.35%; IR, 60.3), lipomas (12.47%; IR, 46.0), hair follicle tumors (12.34%; IR, 45.5), histiocytomas (12.10%; IR, 44.6), soft tissue sarcomas (10.86%; IR, 40.1), and melanocytic tumors (8.63%; IR, 31.8) with >1000 tumors per type. The average IR of all tumor types across the 227 registered breeds was 372.2. The highest tumor incidence was found in the Giant Schnauzer (IR, 1616.3), the Standard Schnauzer (IR, 1545.4), the Magyar Vizsla (IR, 1534.6), the Rhodesian Ridgeback (IR, 1445.0), the Nova Scotia Duck Tolling Retriever (IR, 1351.7), and the Boxer (IR, 1350.0). Mixed-breed dogs (IR, 979.4) had an increased IR compared to the average of all breeds. Previously reported breed predispositions for most tumor types were confirmed. Nevertheless, the data also showed an increased IR for mast cell tumors and melanocytic tumors in the Nova Scotia Duck Tolling Retriever and for histiocytomas in the Flat Coated Retriever. The results from this study can be taken into consideration when selecting purebred dogs for breeding to improve a breed's health.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| 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".