A Rare Case of a Primary Cutaneous Desmoplastic Atypical Granular Cell Tumor
Bibliographic record
Abstract
Granular cell tumors are uncommon neoplasms and a small number of these neoplasms have been reported as showing malignant behavior. Here, we report a rare case of a solitary granular cell tumor that exhibited atypical histology, including an extensive desmoplastic stroma, in a 69-year-old woman. The surgical specimen revealed localized areas of spindling cells, areas of cellular pleomorphism, and p53 overexpression. Based on previously published criteria, we classified this lesion as an atypical granular cell tumor. To date, only very few case reports have documented this desmoplastic variant of granular cell tumor. However, the classifications of benign, atypical, and malignant granular cell tumors are still controversial, owing to an overlap of morphological and immunohistochemical profiles and lack of consistent histological criteria. Additionally, it is unknown whether the histology of the desmoplastic variant in the present case is significant for the classification of granular cell tumors and prediction of patient prognosis. Regardless of these issues, awareness, and close follow-up are required because of potential recurrences of this rare variant of granular cell tumor.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".