Prevalence and pathomorphological features of canine mast cell tumors
Bibliographic record
Abstract
Canine mast cell tumors are among the most common cutaneous neoplasms in dogs, with variable clinical behavior ranging from benign to highly aggressive forms. This study investigated the prevalence and pathomorphological features of mast cell tumors in 42 dogs diagnosed over a seven-month period. Tumor occurrence, clinical presentation, tumor location, cytological characteristics, and histopathological grading were analyzed. The age of occurrence of mast cell tumors ranged from 2 to 14 years, with the highest incidence in age group of 8 to 10 years and lowest incidence in age group of 12 to 14 years, with a mean age of 7.81 years, with a male predominance. Non-descript breeds were the most commonly affected, followed by Labrador and Golden Retrievers. The trunk was the most frequent tumor site. Cytological evaluation revealed characteristic round-to-oval mast cells with metachromatic granules and varying degrees of pleomorphism and anisokaryosis. Infiltration by eosinophils and neutrophils was frequently observed. Histopathological evaluation provided further characterization and grading of the tumors, using two widely accepted systems: Patnaik three-tier classification and the Kiupel two-tier system. Histopathology using Patnaik grading identified Grade II tumors as the most prevalent, while Kiupel grading showed an equal distribution of low-and high-grade tumors. These findings provide essential baseline data for improved diagnostic and therapeutic strategies in canine Mast cell tumors.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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".