Aromatase inhibition: a potential target for the management of recurrent or metastatic endometrial cancer by letrozole: more questions than answers?
Bibliographic record
Abstract
INTRODUCTION: Endometrial cancer generally presents as early and resectable disease, but about 20% of patients present with either incurable or recurrent/metastatic disease. Patients with good performance status will be treated with hormonal agents, including progestins and tamoxifen, followed by cytotoxic chemotherapy. The options are restricted to hormonal agents for those with multiple comorbidities and older age. Therefore, there is a need to identify novel hormonal agents and other targeted therapeutics with improved therapeutic window in this setting. AREA COVERED: Clinical trials of letrozole in localized and metastatic settings are reviewed. In the localized setting, limited by the small sample size, preliminary and conflicting clinical activities were observed. Despite the selection of Type I endometrial cancer, which is more estrogen-dependent for its growth, modest clinical activity was observed in the metastatic setting. Thus far, no biomarkers for efficacy have been identified. EXPERT OPINION: Further understanding of the relevance of aromatase and estrogen receptor and their interplay with other growth pathways will be necessary to guide further development of letrozole. It is premature to declare letrozole a therapeutic option in recurrent/metastatic 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".