Fine needle aspiration cytology (FNAC) Vis-a-Vis histopathology for the diagnosis of canine hepatoid gland carcinoma - A case study
Bibliographic record
Abstract
Hepatoid gland carcinomas (HGC) are relatively uncommon and accounts for 3-7% of all perianal neoplasms in canines. The present communication reports cytological, histopathological and radiographic findings in a rare case of hepatoid gland carcinoma involving tail region in eleven year old uncastrated male Labrador dog. On clinical examination, a solitary, raised, pea sized nodular mass was found over the proximal one third of the base of the tail. A routine complete blood count analysis was performed which showed borderline neutrophilic leucocytosis. Cytologic specimens prepared by Fine Needle Aspiration (FNA) revealed large hepatoid cells in sheets/clusters with vesicular nucleus and hyperchromatic nucleolus. Histopathological examination of excised mass revealed well-differentiated hepatoid cell population characterized by ample cytoplasm, eosinophilic granules and round nuclei with coarse chromatin. Thoracic radiographs revealed evidence of metastases in the lungs. Based on cytological and histopathological findings the mass was diagnosed as hepatoid gland carcinoma. The findings suggest that the FNAC technique could be of great use in the early presumptive diagnosis of canine hepatoid gland tumors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".