Clinical Activity and Safety of the Anti-PD-1 Monoclonal Antibody Dostarlimab for Patients with Recurrent or Advanced dMMR Endometrial Cancer
Bibliographic record
Abstract
This document provides a short summary of the GARNET trial which was published in JAMA Oncology in October 2020. At the end of this document, there are links to websites where you can find more information about this study. The trial enrolled adult participants with advanced solid tumors. This report is restricted to patients with a particular type of endometrial cancer that has a deficient mismatch repair (dMMR) status. Patients received a trial treatment called dostarlimab (also known by the brand name Jemperli). In the US, dostarlimab is approved as a single therapy in adult patients with dMMR recurrent or advanced endometrial cancer that has progressed on or after platinum-based chemotherapy. In the EU, dostarlimab is approved as a single therapy in adult patients with recurrent or advanced dMMR/microsatellite instability-high (MSI-H) endometrial cancer that has progressed on or after treatment with a platinum-containing regimen. The GARNET trial looked at dostarlimab given intravenously to patients with dMMR endometrial cancer from 7 countries. The trial showed that dostarlimab was successful in shrinking the tumor in 42% of these patients. In general, the percentage of participants who experienced medical problems (referred to as side effects) was low and within expectations for this type of treatment. ClinicalTrials.gov NCT number: NCT02715284.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".