Niraparib Maintenance Treatment Improves Time Without Symptoms or Toxicity (TWiST) Versus Routine Surveillance in Recurrent Ovarian Cancer: A TWiST Analysis of the ENGOT-OV16/NOVA Trial
Bibliographic record
Abstract
PURPOSE This study estimated time without symptoms or toxicity (TWiST) with niraparib compared with routine surveillance (RS) in the maintenance treatment of patients with recurrent ovarian cancer. PATIENTS AND METHODS Mean progression-free survival (PFS) was estimated for niraparib and RS by fitting parametric survival distributions to Kaplan-Meier data for 553 patients with recurrent ovarian cancer who were enrolled in the phase III ENGOT-OV16/NOVA trial. Patients were categorized according to the presence or absence of a germline BRCA mutation—g BRCAmut and non-g BRCAmut cohorts. Mean time with toxicity was estimated based on the area under the Kaplan-Meier curve for symptomatic grade 2 or greater fatigue, nausea, and vomiting adverse events (AEs). Time with toxicity was the number of days a patient experienced an AE post–random assignment and before disease progression. TWiST was estimated as the difference between mean PFS and time with toxicity. Uncertainty was explored using alternative PFS estimates and considering all symptomatic grade 2 or greater AEs. RESULTS In the g BRCAmut and non-g BRCAmut cohorts, niraparib treatment resulted in a mean PFS benefit of 3.23 years and 1.44 years, respectively, and a mean time with toxicity of 0.28 years and 0.10 years, respectively, compared with RS. Hence, niraparib treatment resulted in a mean TWiST benefit of 2.95 years and 1.34 years, respectively, compared with RS, which is equivalent to more than four-fold and two-fold increases in mean TWiST between niraparib and RS in the g BRCAmut and non-g BRCAmut cohorts, respectively. This TWiST benefit was consistent across all sensitivity analyses, including modeling PFS over 5-, 10-, and 15-year time horizons. CONCLUSION Patients who were treated with niraparib compared with RS experienced increased mean TWiST. Thus, patients who were treated with niraparib in the ENGOT-OV16/NOVA trial experienced more time without symptoms or symptomatic toxicities compared with control.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".