Breed and anatomical predisposition for canine cutaneous neoplasia in South Africa during 2013
Bibliographic record
Abstract
Cutaneous neoplasia occurs commonly in dogs and owners in consultation with their veterinarian must decide when to perform surgery to obtain a histopathological diagnosis. The objective of this study was to identify breed predispositions for canine cutaneous neoplasms and determine factors associated with malignancy. This retrospective case-series evaluated histopathology reports from two veterinary pathology laboratories in South Africa during a six-month study period. Breed predispositions were analysed using log-linear models and risk factors for malignancy were evaluated using binary logistic regression. Data were available for 2553 cutaneous neoplasms from 2271 dogs. The most frequent neoplasms were mast cell tumours (21.1per cent), histiocytoma (9.4per cent), haemangiosarcoma (8.3per cent), melanocytoma (5.8per cent) and lipoma (5.1per cent). Boxers (relative proportion (RP)=38.9; 95% CI 2.3 to 646), pugs (7.6; 1.4 to 41.0), Staffordshire bull terriers (7.0; 1.9 to 26.3), boerboels (3.8; 1.3 to 10.7), Labrador retrievers (2.7; 1.0 to 7.0) and mixed breed dogs (2.2; 1.1 to 4.4) had a higher frequency of mast cell tumours. Jack Russell terriers (OR=2.5; 95% CI 1.8 to 3.5), Rottweilers (2.3; 1.3 to 3.9), pit bull terriers (2.2; 1.1 to 4.3) and Staffordshire bull terriers (1.6; 1.0 to 2.6) were more likely to have malignant neoplasms. Dog signalment might facilitate prognosis determination for cutaneous canine neoplasia before receiving a histopathological 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".