Myeloid Sarcoma of the Vagina
Bibliographic record
Abstract
OBJECTIVE: To describe 2 cases of myeloid sarcoma of the vagina, in a patient without a history of acute myeloid leukemia (AML) and in another whose condition was previously diagnosed with AML. MATERIALS AND METHODS: The clinical histories of 2 patients whose conditions were diagnosed with myeloid sarcoma of the vagina were obtained from their medical records. RESULTS: Case 1: A 77-year-old woman with no systemic illnesses presented with a vaginal lump. Clinically, there was a 6-cm periurethral mass that was examined by biopsy. The histopathologic specimen was evaluated on routine and immunohistochemical stains, and myeloid sarcoma was diagnosed after extensive immunohistochemical analysis. The patient was treated with pelvic radiation. She developed extensive myeloid sarcoma of the skin and AML 4.5 months later; she died 2 weeks later, 5 months after the initial presentation. Case 2: A 36-year-old woman with a known history of AML who has had multiple leukemic and extramedullary recurrences presented with a pelvic mass. Physical findings revealed large masses in the vagina and rectovaginal septum, which were confirmed as myeloid sarcoma after biopsy and histologic examination. The patient was treated with pelvic/vaginal radiation. Five months later, she had another leukemic relapse and died within 1 day of palliative chemotherapy. CONCLUSIONS: Myeloid sarcoma of the vagina is extremely rare. Most patients have a poor prognosis and either have a history of or will subsequently develop AML.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".