Endometrial cancer: from clinical reality to molecular treatment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Endometrial cancer (EC) is the most prevalent gynecological malignancy in developed countries, with incidence rates steadily increasing due to factors such as obesity, aging populations, and changes in reproductive behavior. While mortality rates have remained relatively stable, survival outcomes have improved little in recent years, underscoring the need for advancements in prevention, diagnosis, and treatment strategies. This review provides a comprehensive overview of both the clinical and fundamental aspects of EC, aiming to bridge the gap between biological research and clinical practice. On the clinical side, we assess genetic predispositions, hormonal and metabolic risk factors, classification systems, detection methods, treatment options, and their impact on survival and quality of life. From a research perspective, we highlight the models commonly used to study EC, including cell lines and animal models, and delve into the PI3K/AKT/mTOR signaling pathway, a critical driver of tumor progression and a promising therapeutic target. By synthesizing these insights, this review aims to inform future efforts to improve patient outcomes and advance the understanding of EC.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it