Squamous cell carcinoma in a dog - A case report
Bibliographic record
Abstract
A 12-year-old male Labrador was presented at a local multispecialty hospital in Chennai with a history of having a multinodular, ulcerated, greyish-white mass on the right forelimb near the digits for surgical removal. Various samples, including blood samples, peripheral blood smears, fine needle aspiration smears of the mass, and the excised mass were collected and sent to the Centralized Clinical Laboratory at Madras Veterinary College in Chennai for laboratory diagnosis. Hematological studies revealed anemia and leukocytosis with left shift neutrophilia, while no changes were observed in serum biochemical parameters. Cytological findings showed clusters of large neoplastic squamous epithelial cells with faintly basophilic cytoplasm, containing prominent vesicular single to multiple nuclei and nucleoli. Additionally, anisocytosis, anisokaryosis, mitotic figures and tadpole cells were suggested, indicative of squamous cell carcinoma. Histopathological examination revealed neoplastic cells that were polygonal in shape, containing prominent vesicular nuclei, mitotic figures and variable-sized keratin pearls. Moreover, immunohistochemical studies showed increased expression of vascular endothelial growth factor (VEGF) in the cytoplasm of the neoplastic cells, confirming the tumor as squamous cell carcinoma. Based on the current laboratory findings, the tumor was identified as well-differentiated squamous cell carcinoma.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".