Lenvatinib and pembrolizumab in patients with advanced uterine cancer
Bibliographic record
Abstract
Uterine cancer is one of the few cancers with an increasing incidence and mortality rate in developed countries, which partly reflects the increasing prevalence of obesity and aging of the female population. Despite the fact that uterine cancer is usually diagnosed when lesions have affected only the uterine body, searching for new treatment regimens for advanced disease remains highly relevant due to unsatisfactory results of chemotherapy and high mortality among women with stage III–IV uterine cancer. In this article, we discuss the main aspects of using lenvatinib and pembrolizumab that have been approved by the US Food and Drug Administration (FDA), Australian Therapeutic Goods Administration (TGA), and Ministry of Health of Canada (HC). The lenvatinib + pembrolizumab scheme showed its efficacy (38.3 % objective responses) and safety (grade III and IV adverse events were reported in 83 (66.9%) of 124 patients) in 94 patients with advanced endometrial cancer whose tumors did not show signs of microsatellite instability (MSI) and defective DNA mismatch repair system. In 25 (69 %) patients who responded to treatment, the time to response was more than 6 months. In this article, we also report a case of progressive uterine cancer in a patient, who benefited from therapy with the combination of lenvatinib and pembrolizumab. We also describe adverse events of such therapy and ways of their management.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".