Primary malignant melanoma of the vagina: A case report
Bibliographic record
Abstract
Malignant primary melanoma of the vagina (PMV) is a rare type of non-cutaneous melanoma often discovered in postmenopausal women. PMV has a very aggressive disease course and a poor prognosis. The best course of treatment is not presently agreed upon. In this report, we describe the case of a 68-year-old woman presenting with a malignant PMV and its subsequent management. The patient presented with right vaginal pain, abnormal vaginal bleeding and a new vaginal mass. A PET scan identified a 28 × 38 mm hypermetabolic vaginal lesion, without nodal involvement or distant metastasis. A posterior exenteration type 2B was performed, including the anal mucosa. Sentinel lymph node dissection, vaginal and rectal resection as well as a terminal colostomy were also carried out. Final staging was FIGO stage III and T4bN1. Four months later, the patient presented with a recurrent vaginal bleed and intra-vaginal induration. Imaging revealed loco-reginal recurrence at the site of the primary malignancy with countless metastases. The patient opted for palliative care given the early disseminated recurrence and her multiple comorbidities. We review the treatment options for PMV, mainly the importance of surgery as the mainstay of treatment as well as the interest of adding checkpoint inhibitor immunotherapy or targeted therapy to the treatment plan. We also summarize the characteristics of PMV and its main prognostic factors.
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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) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".