Exploratory analysis of the effect of maintenance rucaparib on postprogression outcomes in patients (pts) with platinum-sensitive recurrent ovarian carcinoma (OC) and updated safety data from the phase 3 study ARIEL3.
Bibliographic record
Abstract
5522 Background: In ARIEL3, rucaparib maintenance treatment significantly improved progression-free survival (PFS) vs placebo in all predefined, nested cohorts: BRCA mutation; BRCA mutation + wild-type BRCA/high loss of heterozygosity (LOH); and intent-to-treat (ITT) population. Methods: Pts were randomized 2:1 to receive oral rucaparib 600 mg BID or placebo. Exploratory endpoints of time to first subsequent therapy (TFST), time to investigator-assessed PFS on the subsequent line of treatment or death (PFS2), and time to second subsequent therapy (TSST) were assessed in the predefined cohorts. Results: Exploratory efficacy endpoint data are given in the Table. As of Dec 31, 2017, the most common treatment-emergent adverse events (TEAEs) of any grade (rucaparib vs placebo) were nausea (75.8% vs 36.5%), asthenia/fatigue (70.7% vs 44.4%), dysgeusia (39.8% vs 6.9%), and anemia/decreased hemoglobin (39.0% vs 5.3%). The most common grade ≥3 TEAEs were anemia/decreased hemoglobin (21.5% vs 0.5%) and alanine/aspartate aminotransferase increase (10.2% vs 0.0%). Conclusions: Rucaparib significantly improved the clinically meaningful endpoints TFST, PFS2, and TSST vs placebo in all predefined cohorts of pts with platinum-sensitive, recurrent OC. The updated safety profile was consistent with prior reports. Clinical trial information: NCT01968213. [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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".