Survival of large volume recurrent endometrial cancer with peritoneal metastases treated by cytoreductive surgery, HIPEC and EPIC. Report of a case
Bibliographic record
Abstract
INTRODUCTION AND IMPORTANCE: Endometrial cancer may disseminate through lymphatic channels to pelvic and retroperitoneal lymph nodes, through the bloodstream to the lungs, or through the peritoneal space to peritoneal surfaces. However, not all endometrial cancers involve all 3 sites for metastatic disease. CASE PRESENTATION: A patient with large volume of symptomatic recurrence of peritoneal metastases from endometrial cancer was subjected to additional surgery and both regional and systemic chemotherapy. All aspects of her disease and its treatment were studied. CLINICAL DISCUSSION: The primary malignancy was treated by a laparoscopic hysterectomy and bilateral salpingo-oophorectomy followed by intravaginal radiation. Large volume recurrent disease limited to the abdomen and pelvis was treated by complete cytoreductive surgery (CRS), hyperthermic intraperitoneal chemotherapy (HIPEC) and early postoperative intraperitoneal chemotherapy (EPIC). After recovery from surgery, systemic chemotherapy with cisplatin and paclitaxel was administered. The patient is now 25 months following treatment for recurrent cancer and free of disease. CONCLUSIONS: The possibility of complete resection of recurrent endometrial cancer combined with HIPEC, EPIC and systemic chemotherapy is a treatment option for selected patients.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".