An Overview of Endometrial Cancer with Novel Therapeutic Strategies
Bibliographic record
Abstract
Endometrial cancer (EC) stands as the most prevalent gynecologic malignancy. In the past, it was classified based on its hormone sensitivity. However, The Cancer Genome Atlas has categorized EC into four groups, which offers a more objective and reproducible classification and has been shown to have prognostic and therapeutic implications. Hormonally driven EC arises from a precursor lesion known as endometrial hyperplasia, resulting from unopposed estrogen. EC is usually diagnosed through biopsy, followed by surgical staging unless advanced disease is expected. The typical staging consists of a hysterectomy with bilateral salpingo-oophorectomy and sentinel lymph node biopsies, with a preference placed on a minimally invasive approach. The stage of the disease is the most significant prognostic marker. However, factors such as age, histology, grade, myometrial invasion, lymphovascular space invasion, tumor size, peritoneal cytology, hormone receptor status, ploidy and markers, body mass index, and the therapy received all contribute to the prognosis. Treatment is tailored based on the stage and the risk of recurrence. Radiotherapy is primarily used in the early stages, and chemotherapy can be added if high-grade histology or advanced-stage disease is present. The risk of EC recurrence increases with advances in stage. Among the recurrences, vaginal cases exhibit the most favorable response to treatment, typically for radiotherapy. Conversely, the treatment of widespread recurrence is currently palliative and is best managed with chemotherapy or hormonal agents. Most recently, immunotherapy has emerged as a promising treatment for advanced and recurrent EC.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".