Bibliographic record
Abstract
Mast cell tumours (MCT) are the most common malignant skin tumours in dogs. MCTs occur in middleaged dogs with a breed-specific predisposition, e.g. Boxers, Boston Terriers, Weimaraners, Shar-Peis, Golden Retrievers, Labrador Retrievers, Beagles, and Schnauzers. Cutaneous MCT tumours metastasise first to the sentinel lymph nodes and regional lymph nodes and then to distant sites such as the spleen, liver, or bone marrow. The presence of metastases in the lymph nodes (LN), indicating at least stage II disease, is relatively common and a proven negative prognostic indicator [1]. Early detection of LN metastases is crucial for prognosis and better patient care. Accurate detection of metastasised LN prior to surgical excision is difficult. Fine needle aspirates of LN for the detection of MCT metastases have a low sensitivity of 31% [2]. Dogs with low-grade MCTs have a good prognosis after surgical excision of the primary tumour and elective lymphadenectomy of the early metastatic regional LN [3]. Sentinel, lymph node mapping is therefore very important. The mapping of sentinel lymph nodes is described: lymphoscintigraphy, colourimetric SLN mapping (using the peritumour injection of blue dye or indocyanine green), radiological lymphography (also known as direct lymphography or radiography), indirect lymphangiography, computed tomography lymphangiography (CTL), near-infrared fluorescence/near-infrared fluorescence-guided lymphography (NIR/NIR-LND) and contrast-enhanced ultrasound (CEUS) [4].
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
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 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".