Pelvic Aggressive Angiomyxoma: Major Challenges in Diagnosis and Treatment
Bibliographic record
Abstract
Aggressive pelvic angiomyxoma is a very rare mesenchymal tumor that is usually diagnosed in premenopausal female patients. The current mainly reported treatment is wide surgical excision. Other treatment options, such as radiotherapy and hormonal therapy, have been suggested as potential alternatives. A 61-year-old postmenopausal female patient presented with hematuria that led to the identification of a perirectal mass on abdominopelvic imaging. A 46-year-old female patient presented with a perineal mass of unknown etiology. Despite extensive investigations, the diagnosis could not be confirmed before surgical resection in both patients. Surgical excisions were performed and revealed the presence of an aggressive angiomyxoma with positive estrogen and progesterone tumoral receptors in both cases. Radiological and clinical recurrence was noted in one patient. Tumor regression was noted in this patient after treatment with a luteinizing hormone-releasing hormone (LHRH) agonist with long-term remission. The diagnosis of a perirectal aggressive angiomyxoma is an exceedingly rare event. Preoperative biopsy and pathological diagnosis are challenging and often yields poor results. Its slow growth and expression of hormonal receptors make noninvasive therapeutic strategies, such as radiotherapy, gonadotropin-releasing hormone agonists, or even watchful waiting, valid options in selected patients. Due to the lack of reported cases, the best treatment has yet to be elucidated.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.007 |
| 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".