Mast Cell Tumour and Mammary Gland Carcinoma Collision Tumour. Case report and literature review.
Bibliographic record
Abstract
Collision tumours are the coexistence, at the same venue, of distinct tumours not macroscopically distinguishable and consisting of two independent cell populations without histological admixture. In human medicine, collision tumours in different anatomical sites have been described. In the veterinary literature, few cases exist so far. A 12-year-old female Labrador with a mammary gland nodular lesion was presented for clinical examination. The nodule was surgically removed and underwent histological and immunohistochemical analysis. Histopathological examination revealed two distinct malignant tumours: a mammary gland carcinoma and a cutaneous mast cells tumour. To the author's knowledge, the paper reports the first case of a collision tumour composed of mammary gland neoplasia and mast cell tumour. The rising interest in collision tumours suggests widening their knowledge and setting up a multimodal approach that includes surgery and targeted therapy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".