Case report of a breast granular cell tumor in a young transgender man
Bibliographic record
Abstract
INTRODUCTION AND IMPORTANCE: Granular cell tumors (GCTs) can be diagnostically challenging due to their rarity, diverse anatomic locations, and clinical and radiologic similarities to other more common entities. GCTs involving the breast are rare and are most commonly encountered in premenopausal cisgender women. We report an unusual case of a breast GCT in a young transgender man. CASE PRESENTATION: A 20-year-old transgender man who was on testosterone therapy for about 1 year presented with a painless, palpable mass in the right breast which radiologically resembled a lymph node. A fine needle aspiration showed morphology and immunohistochemistry consistent with a GCT. The tumor was excised by a mastectomy for therapeutic and gender-affirming purposes which confirmed the diagnosis of a breast GCT. CLINICAL DISCUSSION: Breast GCTs are most commonly found in cisgender women, however the mechanisms behind this relationship and whether transgender persons have an altered risk profile are not well understood. Breast GCTs are typically benign lesions with a low chance of recurrence following excision. CONCLUSION: GCTs are rare and poorly understood entities which have not been previously documented in transgender patients and can resemble other benign or malignant lesions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".