Treatment strategies for endometrial cancer: current practice and perspective
Bibliographic record
Abstract
PURPOSE OF REVIEW: Endometrial cancer incidence is increasing in North America and is a major cause of morbidity and mortality in women. We review recent literature published on treatment of endometrial cancer and highlight areas of active interest. RECENT FINDINGS: There has been movement toward minimal invasive surgery at diagnosis; lymph node staging remains controversial and continues to be investigated. Progress has been made to establish consensus on endometrial cancer risk classification to promote consistency for future trial design. Molecular characterization of endometrial cancer and its integration into clinicopathological profiling to develop predictive biomarkers for treatment selection are active areas of research. Optimal adjuvant treatment strategy in high-risk endometrial cancer remains to be defined with recognition of treatment-related toxicity. Despite encouraging results in drug development for treatment of advanced/recurrent endometrial cancer, no targeted therapies beyond hormonal therapy are approved. There is an urgent need for scientifically validated therapy with predictive biomarkers. SUMMARY: Our understanding of endometrial cancer has evolved through improvements in molecular biology, allowing improved definition of target-specific therapies. The precise role and sequence of conventional and targeted therapies, including immunotherapy, will require careful attention to the design of clinical trials with translational emphasis to allow the discovery, validation, and implementation of predictive biomarkers into clinical care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".