Recent advances in systemic therapy for advanced endometrial cancer
Bibliographic record
Abstract
PURPOSE OF REVIEW: Endometrial cancer is the most common gynaecologic cancer in the western world. Systemic treatments for advanced disease have traditionally included hormonal therapy and chemotherapy. Responses to treatment are short-lived and advanced-stage disease remains incurable. Recent research has focused on optimizing chemotherapy regimens, the development of alternative hormonal therapy strategies and the introduction of targeted therapies. The most recent developments in these areas will be reviewed here. RECENT FINDINGS: Phase III trials continue to focus on the optimization of combination chemotherapy regimens. The elucidation of a hormonal pathway central to the control of oestrogen-stimulated cancer growth has led to the development of a new class of hormonal agents currently undergoing evaluation in the clinical trial setting. Increasing understanding of the molecular basis for malignant transformation continues to provide rationale for the development of many targeted therapies. Mammalian target of rapamycin inhibition, in particular, offers further encouraging results in this context. SUMMARY: The development of new hormone treatments and effective targeted therapies will provide new opportunities to improve therapy for women with advanced endometrial cancer. Optimization of therapy will require an approach to personalized therapy in order to guide choice and sequence of therapy and improve survival and quality of life.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".