Dostarlimab and niraparib in primary advanced ovarian cancer
Bibliographic record
Abstract
BACKGROUND: The combination of immunotherapies and poly (ADP-ribose) polymerase inhibitors (PARPis) has been hypothesized to improve outcomes in advanced ovarian cancer (aOC). The FIRST/ENGOT-OV44 trial evaluated adding dostarlimab to first-line platinum-based chemotherapy (PBCT) and niraparib maintenance ± bevacizumab in patients with aOC. PATIENTS AND METHODS: In this randomized, double-blind, phase III trial, patients with newly diagnosed stage III-IV epithelial OC were randomized (1:2) to arm 2 (PBCT-placebo with niraparib maintenance) or arm 3 (PBCT-dostarlimab with dostarlimab-niraparib maintenance); arm 1 (PBCT-placebo with placebo maintenance) enrollment terminated following PARPi approvals. Efficacy was assessed in arms 2 and 3 (intention-to-treat population). The primary endpoint was investigator-assessed progression-free survival (PFS) as per RECIST v1.1. The key secondary endpoint was overall survival (OS). Safety was assessed in patients who received one or more doses of study treatment (arms 1-3; analyzed as per treatment received). RESULTS: From 14 November 2018 to 5 January 2021, 1138 patients were randomized to arms 2 (n = 385) and 3 (n = 753) and included in efficacy analyses. Median follow-up was 53.1 (interquartile range 47.5-59.7) months. There was a statistically significant difference in PFS in arm 3 versus arm 2 (median 20.6 versus 19.2 months; hazard ratio [HR] 0.85, 95% confidence interval [CI] 0.73-0.99, P = 0.0351). OS had reached 57% maturity and was not statistically significant (median 44.4 versus 45.4 months; HR 1.01, 95% CI 0.86-1.19, P = 0.9060). Toxicities observed were consistent with known safety profiles of the agents used in the study. CONCLUSIONS: In the first-line treatment of patients with aOC, the addition of dostarlimab to PBCT and niraparib maintenance was associated with a statistically significant, but clinically modest, PFS improvement, with no difference in OS.
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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) | 0.003 | 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".