Maintenance therapy with a poly(ADP-ribose) polymerase inhibitor (PARP) in patients with newly diagnosed ovarian cancer: A systematic review and meta-analysis.
Bibliographic record
Abstract
e17587 Background: PARP inhibitors (PARPi) have been approved for maintenance therapy in newly diagnosed ovarian cancer patients with or without homologous recombination deficiency (HRD). In this meta-analysis, the latest clinical data to assess the efficacy and safety of this approach across different subgroups were synthesized. Methods: PubMed, Scopus, and Cochrane databases were searched for randomized controlled trials that compared maintenance PARPi with placebo in newly diagnosed ovarian cancer. The outcomes of interest included progression-free survival (PFS) according to HRD status, overall survival (OS), second PFS (PFS2), and time-to-first subsequent treatment (TFST). Heterogeneity was examined with I2 statistics. A random-effects model was used for outcomes with high heterogeneity. Results: A total of 6 Randomized clinical trials with 3609 patients were included, of whom 2348 (65%) received maintenance PARPi. PFS was significantly longer for HRD-positive patients receiving PARPi [Hazard ratio (HR) 0.44, 95% Confidence interval (CI) 0.37 – 0.52 p<0.001] and for HRD-negative patients [HR 0.71, 95% CI 0.54 – 0.92 p=0.009]. Maintenance PARPi was associated with improved OS in HRD-positive patients [HR 0.59, CI 0.47 -0.73, p<0.001] but not in unselected patients. PFS2 in the HRD-positive population was reported in three trials, and it favored the use of PARPi [HR 0.57, CI 0.43 – 0.76, p<0.001]. Adverse events ≥grade 3 were more common in patients on maintenance PARPi (p<0.001). The occurrence of myelodysplastic syndrome was numerically but not significantly higher in the PARPi group. Conclusions: Maintenance with PARPi in patients with newly diagnosed ovarian cancer significantly prolongs PFS regardless of HRD status. Moreover, PARPi maintenance improves OS and PFS2 in patients with BRCA and/or HRD-positive tumors compared to placebo. [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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.034 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".