Canine Subcutaneous Mast Cell Tumor
Bibliographic record
Abstract
Histologic grading schemes for canine cutaneous mast cell tumors (MCTs) were not developed for subcutaneous MCTs. Despite this, subcutaneous MCTs are currently categorized by many as grade II or higher. The aim of this investigation was to assess the pathology and clinical outcome for subcutaneous MCTs to provide a more accurate prognosis. Information on clinical outcome for 306 dogs was obtained from veterinarians and correlated with histologic features. Mean and median follow-up was 842 and 891 days, respectively (range, 3-2,305 days). Only 27 (9%) were confirmed as mast cell-related deaths. Metastasis occurred in 13 (4%), and 24 (8%) had local reoccurrence, even though 171 (56%) cases had incomplete surgical margins. Median survival time was not reached, and the estimated 6-month, 1-, 2-, and 5-year survival probabilities were 95%, 93%, 92%, and 86%, respectively. Dogs were euthanized or died as a result of local tumor reoccurrence, additional MCT development distant to the surgical site, or metastasis. Decreased survival time was linked to mitotic index (number of mitotic figures per 10 high-power fields), infiltrative growth pattern, and presence of multinucleation. Both univariable and multivariable analysis showed mitotic index to be strongly predictive of survival, local reoccurrence, and metastasis. The results of the study indicate that the majority of subcutaneous MCTs have a favorable prognosis, with extended survival times and low rates of reoccurrence and metastasis.
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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 | 0.001 |
| 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 | 0.000 | 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".