Multi-modal Management of an Oral Amelanotic Malignant Melanoma in a Labrador Retriever
Bibliographic record
Abstract
A ten-year-old, intact male, Labrador Retriever was presented to the Cornell University Hospital for Animals' Oncology Service for evaluation of a recurrent oral amelanotic melanoma. This lesion was first noticed by his owners in early March on the right buccal mucosa. The mass had been previously excised by the primary veterinarian. Histology revealed an incompletely excised amelanotic melanoma with a mitotic index of 52 per l0HPF. Staging at CUHA revealed no evidence of metastasis so computed tomographic imaging of the head was performed for surgical planning. A surgical scar revision was performed, along with removal of both mandibular lymph nodes. Samples from the surgical margins were submitted for intraoperative cryosections which revealed clean margins. The mucosa was reconstructed with local mucosa! flaps. At a recheck appointment with the Oncology Service two months following the scar revision, the patient had repeat thoracic radiographs taken and revealed a possible pulmonary nodule, which was later determined to be summation of normal tissue. Three and a half months after the surgical scar revision, the patient presented to the Emergency Service for progressive hind-end weakness and a two day history of inappetance and inability to stand. Imaging revealed evidence of metastasis in the liver and, given a poor prognosis, euthanasia was elected. This report will discuss oral amelanotic melanoma in dogs, common clinical presentations, diagnostics, and potential treatment options for the disease.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| 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".