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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".