Incidence and Histopathology of Sebaceous Gland Tumors in Dogs
Bibliographic record
Abstract
The study was aimed to know the prevalence of sebaceous gland tumor in canine and its classification based on histopathology. A total of 569 biopsy samples of canines suspected for neoplastic growth received by Department of Veterinary Pathology from TVCC departments of College of Veterinary Science and A.H. Anand over last 16 years (2001 to 2017) were analyzed. The biopsy samples were subjected to histopathological examinations by using H and E staining. Out of 569 biopsy samples, sebaceous gland growth was observed in 23 (4.04%) cases. The highest incidence (39.13%) was recorded for 9 to 12 years of age. The incidence of sebaceous gland tumor was observed in all the breeds; however, the Labrador retriever was the most affected breed followed by Pomeranian, non-descript and others. The frequent site for sebaceous gland tumor was eyelid. Out of 23 sebaceous gland tumors, one hyperplasia, 19 adenomas, two , and one adenocarcinoma were observed, suggesting that sebaceous gland adenoma was the most common type of sebaceous gland tumors in canine.
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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 | 0.001 | 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".