Histological and Immunohistochemical Features of Pulmonary Metastatic Oral Melanoma in a Labrador dog
Bibliographic record
Abstract
Melanoma is a malignant tumour that originates from melanocytes. It has been reported in human beings as well as in many domesticated animal species (Reddy et al., 1998), and wild terrestrial and marine animals. Melanomas are the most commonly diagnosed malignant tumours in the oral cavity of canines (Goldschmidt, 1985; Faramade et al., 2017). Gingiva is the most common site for canine oral malignant melanoma (OMM) but other parts like palatine, labile or buccal mucosa also act as the sites of origin (Delverdier et al., 1991). It is generally an aggressive tumour, often locally invasive, and frequently metastasizes to regional lymph nodes and lungs but metastasis to other organs like the brain, heart, spleen, and liver is not common (Goldschmidt and Hendrick, 2002). Canine OMM accounts for about 7% of all malignant tumours of canine, 11.5% to 17.1% of all oral tumours (Mikiewicz et al., 2019), and 33% to 35.8% of all malignant oral tumours (Sarowitz et al., 2017). OMM is reported in old age group animals mainly ranging from 7 to 14 years age (Esplin, 2008). Most common breeds affected by OMM include Cocker Spaniels, Golden Retrievers, Dachshunds, mixed-breed dogs (Gillard et al., 2014) but histologically well-differentiated melanocytic neoplasms (HWDMN) also reported in Golden Retrievers, Labrador, Doberman Pinscher, Irish Setters, Cocker Spaniels, Beagles, etc. (Esplin, 2008). The diagnosis of melanoma is difficult mainly in tumors without appreciable melanin. Histological appearance resembles carcinoma, lymphoma, sarcoma, and osteogenic tumours. Therefore, immunohistochemistry with numerous melanoma specific markers is mostly used for confirmatory diagnosis in human and veterinary pathology (Wick, 2008). This case report is on the occurrence of oral melanoma with pulmonary metastasis in a Labrador dog.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".