What Has Changed in the Management of Uterine Serous Carcinomas? Two Decades of Experience
Bibliographic record
Abstract
Uterine serous carcinoma accounts for 3–10% of endometrial cancers, but it is the most lethal histopathological subtype. The molecular characterization of endometrial carcinomas has allowed novel therapeutic approaches for these patients. We undertook a retrospective analysis of patients with uterine serous carcinomas treated in our hospital within the last two decades to identify possible changes in their management. The patients and their characteristics were evenly distributed across the two decades. Treatment modalities did not change significantly throughout this period. After adjuvant treatment, patients’ median disease-free survival was 42.07 months (95% CI: 20.28–63.85), and it did not differ significantly between the two decades (p = 0.059). The median overall survival was 47.51 months (95% Cl: 32.18–62.83), and it significantly favored the first decade’s patients (p = 0.024). In patients with de novo metastatic or recurrent disease, median progression-free survival was 7.8 months (95% Cl: 5.81–9.93), whereas both the median progression-free survival and the median overall survival of these patients did not show any significant improvement during the examined time period. Overall, the results of our study explore the minor changes in respect of uterine serous carcinoma’s treatment over the last two decades, which are reflected in the survival outcomes of these patients and consequently underline the critical need for therapeutic advances in the near future.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".