Non-Metastatic Uterine Carcinosarcoma: A Tailored Approach or One Size Fits All?
Bibliographic record
Abstract
Purpose: Uterine carcinosarcomas are highly aggressive tumors of the endometrium and are associated with a poor prognosis. The optimal adjuvant treatment for both early and advanced-stage patients remains unclear. Methods: Cases of uterine carcinosarcoma were identified in our institution’s pathology database between 2000 and 2022. Kaplan–Meier estimates were calculated for the local and distant recurrence-free, disease-free and overall survival; hazard ratios were calculated using Cox proportional hazards modelling for independent prognostic factors including the stage and treatment. Results: A total of 48 patients were identified as having uterine carcinosarcoma, of whom 70.8% were surgically staged. In total, 43 patients had pelvic-confined disease, while five had positive omental or peritoneal biopsies at surgery. There were 10 pelvic (20.8%) and 19 (39.6%) distant recurrences. None of the patients with stage IA disease who received chemotherapy and brachytherapy experienced disease recurrence. The local recurrence-free survival was 54.95%, the distant recurrence-free survival was 44.7%, and the overall survival was 59.6% at 5 years. Local recurrence-free survival and overall survival were inversely associated with advanced-stage OR 1.23 (p = 0.005) and OR 1.28 (p = 0.017), respectively, and no chemotherapy was associated with OR 1.96 (p = 0.06) and OR 2.08 (p = 0.056), respectively. Conclusion: The local and distant recurrence rates were high for advanced=stage patients even when treated with aggressive adjuvant therapy regimens. Chemotherapy may improve recurrence and survival. Early-stage patients may perform well with vaginal vault brachytherapy and chemotherapy. Further prospective comparisons are required between sequential, sandwich, and concurrent approaches to chemotherapy and radiotherapy, to optimize outcomes in this high-risk population.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".