Pharmacotherapy of endometrial cancer
Bibliographic record
Abstract
BACKGROUND: Endometrial cancer is the seventh most common malignancy among women worldwide. Despite most cases being diagnosed at an early stage, the death rate has increased steadily over the past 20 years. The lack of an effective, standardized adjuvant treatment for women at a high risk of recurrence has contributed to these disappointing results. OBJECTIVES: The goal of this review was to assess the role of hormonal and cytotoxic therapies in the adjuvant treatment of endometrial cancer. Once defined, an evidence-based management algorithm for this neoplasm was proposed. METHODS: A thorough literature search was undertaken using the Cochrane and Pubmed databases. Systematic reviews, meta-analyses, and randomized controlled trials were first collected and critically analyzed. Other study types were secondarily considered when pertinent. CONCLUSIONS: The choice of the adjuvant therapy in early-stage endometrial cancer must be a patient-specific decision. Preliminary data suggest a role for chemotherapy in high-risk subgroups. However, further research is necessary to confirm this. To date, hormonal therapy has not been widely used in the management of early-stage disease, other than for conservative treatment in a fertility-sparing setting. Both hormonal and chemotherapy represent valuable therapeutic tools for the management of patients affected by advanced disease.
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 | 0.002 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".