Novel Molecular Targets in Endometrial Cancer: Mechanisms and Perspectives for Therapy
Bibliographic record
Abstract
Pamela Soberanis Pina, Stephanie Lheureux Department of Medical Oncology, Princess Margaret Cancer Centre, Toronto, ON, CanadaCorrespondence: Stephanie Lheureux, Princess Margaret Cancer Centre, 700 University Avenue, Toronto, M5G 1Z5, Ontario, Canada, Email Stephanie.Lheureux@uhn.caAbstract: Endometrial cancer (EC) has a high epidemiological impact with incidence and mortality rising worldwide. In recent years, the integration of the pathologic and molecular classification has provided relevant information to understand the heterogeneity in the biology of EC, which led to the evolution in the management of patients. Currently, therapeutic breakthroughs have been made in advanced EC to improve oncologic outcomes, with efforts to include patient reported outcomes. Precision and personalized medicine are under way in EC exploring different combination approaches to target cross-talk pathways, cancer cell microenvironment, and metabolic vulnerabilities and improve drug delivery. Yet, collaborative efforts are needed to face the challenges in practice by refining patient selection, ideal biomarker identification, and de-escalation of therapies according to emerging molecular and genomic features of EC.Keywords: endometrial cancer, targeted therapy, molecular targets, precision medicine
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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 | 0.002 | 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 | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".