First-line treatments for advanced or recurrent endometrial cancer: Systematic literature review of clinical evidence
Bibliographic record
Abstract
Novel therapies are driving meaningful changes to the management of endometrial cancer (EC). Herein, a systematic literature review was conducted to evaluate the efficacy and safety of first-line treatments for advanced/recurrent EC. Searches were conducted using multiple databases through October 26, 2023. In total, 108 records of 57 unique trials (48 of first-line therapies) met the inclusion criteria. Baseline characteristics varied by study, and sample sizes ranged from 28 to 1328. Median progression-free survival was reported in 28 trials (range, 1.9–18.8 months), median overall survival in 26 trials with mature data (range, 6.9–41 months), and safety in 21 trials evaluating first-line systemic therapy ± maintenance. The potentially high risk of adverse events may outweigh the suboptimal efficacy benefits reported for conventional chemotherapy or hormonal therapies. The safety and efficacy of immunotherapies identified within are expected to contribute to a paradigm shift in the management of primary advanced/recurrent EC. • The efficacy of conventional chemotherapy and hormonal therapies is suboptimal. • Increasing the number of agents used in combination increases toxicity. • Molecular profiling was limited but is increasing over time. • Progression-free survival was improved in recent immunotherapy + chemotherapy trials. • The standard of care is evolving for primary advanced/recurrent endometrial cancer.
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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".