Fertility-Sparing Treatment of Patients with Endometrial Cancer: A Review of the Literature
Bibliographic record
Abstract
Endometrial cancer (EC) is currently the most common malignancy of the female genital tract in developed countries. Although it is more common in postmenopausal women, it may affect up to 25% in the premenopausal age and 3-5% under the age of 40 years. Furthermore, in the last decades a significant shift to pregnancy at older maternal ages, particularly in resource-rich countries, has been observed. Therefore, in this scenario fertility-sparing alternatives should be discussed with patients affected by EC. This study summarizes available literature on fertility-sparing management of patients affected by EC, focusing on the oncologic and reproductive outcomes. A systematic computerized search of the literature was performed in two electronic databases (PubMed and MEDLINE) in order to identify relevant articles to be included for the purpose of this systematic review. On the basis of available evidence, fertility-sparing alternatives are oral progestins alone or in combination with other drugs, levonorgestrel intrauterine system and hysteroscopic resection in association with progestin therapies. These strategies seem feasible and safe for young patients with G1 endometrioid EC limited to the endometrium. However, there is a lack of high-quality evidence on the efficacy and safety of fertility-sparing treatments and future well-designed studies are required.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| 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) | 0.003 | 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".