Prevalence and pathological features of canine mammary tumors
Bibliographic record
Abstract
The present study examined 50 spontaneous canine mammary tumors to assess their distribution based on age, breed, and glandular involvement, along with their gross, cytological, and histopathological features. Mammary tumors were most frequently observed in middle-aged female dogs, with a mean age of eight years and a peak incidence between six and eight years. Labrador Retrievers were the most commonly affected breed, followed by non-descriptive breeds, Golden Retrievers, and Dachshunds. All affected dogs were females, predominantly intact, indicating a strong hormonal influence in tumor development. The caudal abdominal and inguinal glands, especially on the left mammary chain, were the most frequently involved sites. Grossly, the tumors varied from well-circumscribed solitary nodules to large, multifocal, ulcerated, or cystic masses. Malignant tumors accounted for 70% of the total cases, while benign and hyperplastic lesions comprised the remainder. Cytological evaluation of malignant tumors revealed high cellularity, marked pleomorphism, and a high nuclear-to-cytoplasmic ratio, whereas benign tumors exhibited cellular uniformity and low mitotic activity. Histopathologically, tubular carcinoma was the most prevalent malignant subtype, followed by complex and cystic papillary carcinomas, while simple adenomas were the most common benign lesions. The observed histological patterns were consistent with established classifications of canine mammary neoplasia, validating the use of cytology as a reliable diagnostic tool. Overall, the findings emphasize that spontaneous mammary tumors are common in intact, middle-aged female dogs, with the caudal mammary glands being the most affected sites, highlighting the importance of early cytological screening and histopathological evaluation for accurate diagnosis and management of canine mammary neoplasia.
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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 | 0.001 |
| 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".