Time course of adverse events in primary advanced or recurrent endometrial cancer treated with dostarlimab plus chemotherapy in the ENGOT-EN-6-NSGO/GOG-3031/RUBY trial.
Bibliographic record
Abstract
5607 Background: Dostarlimab (DOST)+carboplatin-paclitaxel (CP) significantly improved PFS and OS vs CP alone in patients (pts) with primary advanced or recurrent endometrial cancer (pA/rEC) in the phase 3RUBY trial (NCT03981796). Safety has been reported for immunotherapy+chemotherapy combinations in EC, though timing of adverse events (AEs) and the longer-term AE profile is not yet clear. This analysis examines the time course of AEs related to any study treatment (TRAEs) during the RUBY trial. Methods: Pts with pA/rEC were randomized 1:1 to DOST+CP or placebo (PBO)+CP Q3W (6 cycles), followed by DOST or PBO monotherapy Q6W for up to 3 years. AEs were assessed according to CTCAE v4.03 and summarized by quarter. Results: The safety population included 487 pts who received ≥1 dose of treatment (241 DOST+CP; 246 PBO+CP). TRAEs were experienced by 97.9% of pts in the DOST+CP arm and 98.8% of pts in the PBO+CP arm. In both arms, the majority of the most common TRAEs (≥30%) and grade ≥3 TRAEs (≥10%) occurred within the first 3 to 6 months of treatment (Table). The timing of immune-related AEs (irAEs) was generally consistent with this finding. Conclusions: The majority of TRAEs seen in the RUBY trial occurred within the first 3 to 6 months, with limited differences between arms. This timing is consistent with the pts’ receipt of chemotherapy. Few pts experienced an onset of new TRAEs in the DOST+CP arm after 12 months. These safety data further support a favorable long-term benefit-risk profile of dostarlimab+CP in pts with primary advanced or recurrent EC. Clinical trial information: NCT03981796 . [Table: see text]
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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 | 0.003 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".