Stage I Clear Cell and Serous Uterine Carcinoma: What Is the Right Adjuvant Therapy?
Bibliographic record
Abstract
This single-center study aimed to retrospectively evaluate the survival outcomes of patients with FIGO stage I clear cell and serous uterine carcinoma according to the type of adjuvant treatment received. The data were collected between 2003 and 2020 and only patients with stage I clear cell or serous uterine carcinoma treated with primary surgery were included. These were classified into three groups: No treatment or brachytherapy only (G1), radiotherapy +/− brachytherapy (G2), chemotherapy +/− radiotherapy +/− brachytherapy (G3). In total, we included 52 patients: 18 patients in G1, 16 in G2, and 18 in G3. Patients in the G3 group presented with poorer prognostic factors: 83.3% had serous histology, 27.8% LVSI, and 27.8% were FIGO stage IB. Patients treated with adjuvant radiotherapy showed an improved 5-year overall survival (OS) (p = 0.02) and a trend towards an enhanced 5-year progression-free survival (PFS) (p = 0.056). In contrast, OS (p = 0.97) and PFS (p = 0.84) in the chemotherapy group with poorer prognostic factors, were similar with increased toxicity (83.3%). Radiotherapy is associated with improved 5-year OS and tends to improve 5-year PFS in women with stage I clear cell and serous uterine carcinoma. Additional chemotherapy should be cautiously considered in serous carcinoma cases presenting poor histological 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".