Canine Subcutaneous Mast Cell Tumors
Bibliographic record
Abstract
Molecular assays are widely used to prognosticate canine cutaneous mast cell tumors (MCT). There is limited information about these prognostic assays used on MCT that arise in the subcutis. The aims of this study were to evaluate the utility of KIT immunohistochemical labeling pattern, c-KIT mutational status (presence of internal tandem duplications in exon 11), and proliferation markers--including mitotic index, Ki67, and argyrophilic nucleolar organizing regions (AgNOR)--as independent prognostic markers for local recurrence and/or metastasis in canine subcutaneous MCT. A case-control design was used to analyze 60 subcutaneous MCT from 60 dogs, consisting of 24 dogs with subsequent local recurrence and 12 dogs with metastasis, as compared to dogs matched by breed, age, and sex with subcutaneous MCT that did not experience these events. Mitotic index, Ki67, the combination of Ki67 and AgNOR, and KIT cellular localization pattern were significantly associated with local recurrence and metastasis, thereby demonstrating their prognostic value for subcutaneous MCT. No internal tandem duplication mutations were detected in exon 11 of c-KIT in any tumors. Because c-KIT mutations have been demonstrated in only 20 to 30% of cutaneous MCT and primarily in tumors of higher grade, the number of subcutaneous MCT analyzed in this study may be insufficient to draw conclusions on the role c-KIT mutations in these tumors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".