Patient-reported outcomes (PROs) in patients (pts) with primary advanced or recurrent endometrial cancer (pA/rEC) treated with dostarlimab (D) plus carboplatin/paclitaxel (CP) compared with CP in the ENGOT-EN6/GOG3031/RUBY trial.
Bibliographic record
Abstract
332 Background: In RUBY (NCT03981796), a phase 3, global, randomized, double-blind trial, D+CP demonstrated significant and clinically meaningful improvement in PFS vs placebo + CP (P+CP) in pA/rEC. Methods: 494 pts with pA/rEC were randomized 1:1 to D+CP or P+CP Q3W for 6 cycles (C), followed by D or placebo monotherapy Q6W for ≤2 y. EORTC QLQ-C30 and EN24 (prespecified secondary endpoints) were administered on day 1 of each C, at end of treatment (EOT), and at safety and survival follow-ups. Change (chg) from baseline (BL) to C7 (end of chemotherapy) and C13 (1 y) was calculated in the overall and mismatch repair–deficient (dMMR)/microsatellite instability–high (MSI-H) populations. Least-squares means (LSM) were generated using mixed models for repeated measures, adjusting for correlations across multiple assessments within a pt and controlling for BL global, pain, fatigue, and physical function (PF) scores. Results: In the overall population, PROs were similar with D+CP and P+CP through C7, and no differences between arms across the 3-y period were reported; mean chg from BL to EOT showed numerical improvement with D+CP in back/pelvic pain and deterioration with P+CP in global QOL/GHS, social function (SF), body image, and chg in taste. In the dMMR/MSI-H population, at C7, nominally significant improvements in QOL, PF, role function (RF), pain, and back/pelvic pain were seen with D+CP vs P+CP (Table). Conclusions: Dostarlimab+CP significantly improved PFS and maintained HRQOL in dMMR/MSI-H and overall populations, further supporting its use as a standard of care in pA/rEC. 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| 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 | 0.001 | 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".