Clinical outcomes of dogs with high-grade cutaneous mast cell tumors
Bibliographic record
Abstract
Objectives: To evaluate the prognostic factors and treatment outcomes in dogs with high-grade cutaneous mast cell tumors (HGMCTs). Methods: Medical records of dogs with a histopathologic diagnosis of HGMCTs were reviewed from a single institution. Clinical factors, treatment-related variables, and adjuvant therapies were documented to evaluate their association with clinical outcomes. Comparative and survival analyses were conducted using Kaplan-Meier survival analysis, log-rank, and Fisher's exact tests. Results: The overall median survival time for the 77 dogs was 317 days (range 20-3,041 days) with 6-month, 1-year, and 2-year survival rates of 69, 50, and 30%, respectively. Surgically treated dogs had significantly prolonged survival and were 6.88 times more likely to survive beyond 5.5 months. The presence of metastasis at initial staging was strongly associated with poorer outcomes, as dogs without metastasis at initial staging had 6.94 times higher odds of surviving beyond 2 years. Surgical sites with incomplete margins had a higher local recurrence rate (58%) compared to those with clean margins (26%). Despite aggressive treatment, 75% of the dogs that received concurrent surgical and adjuvant therapy experienced disease progression. Lymph node extirpation, tumor localization, number of tumors, and local recurrence were not associated with the overall outcome. Clinical relevance: The combination of aggressive local therapy and adjuvant systemic chemotherapy provides a notable survival benefit in dogs with HGMCTs. The limited therapeutic benefit of locoregional lymph node extirpation, combined with a persistently high metastatic rate despite systemic chemotherapy, highlights the critical need for more effective regional and systemic treatment approaches for HGMCT patients.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".