Recent and current Phase II clinical trials in endometrial cancer: review of the state of art
Bibliographic record
Abstract
INTRODUCTION: Endometrial cancer (EC) is the most common gynecological cancer in the developed world. For women with advanced or high-risk disease, survival has remained unchanged over the last 20 years highlighting the need for better therapies. Phase II trials are critical to ascertain an estimate of benefit and determine which new agents undergo further development. AREAS COVERED: Based on a literature search of MEDLINE and ASCO over the last 5 years, the authors present Phase II clinical trial data in the context of EC management. They highlight ongoing clinical trials from the National Cancer Institute website and suggest future directions to address ongoing questions. EXPERT OPINION: A better understanding of EC biology and high-quality preclinical studies will inform the future design of EC Phase II studies. Inclusion of correlative studies and continued longitudinal profiling in future trials is essential to elucidate mechanisms of drug resistance and response. Targeting the phosphoinisotol-3-kinase, angiogenesis, DNA repair and metabolic pathways appear promising strategies for subsets of patients with recurrent or advanced disease. Further, investigation of maintenance strategies and radio-sensitizing agents in the frontline setting should be explored. Given the patient demographic, and frequency of co-morbidities, tolerability and quality of life are key be considerations when designing future studies.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".