Prevalence and pathomorphological features of canine mast cell tumors
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.003 |
| 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 it