Clinical Outcomes of Poly(ADP–Ribose) Polymerase Inhibitors as Maintenance Therapy in Patients with Ovarian Cancer in the Southeastern Region of Korea
Bibliographic record
Abstract
Purpose: In this study, we aimed to retrospectively investigate the real-world clinical efficacy and adverse events of poly(adenosine diphosphate [ADP]-ribose) polymerase (PARP) inhibitors in real-world clinical practice among patients with newly diagnosed epithelial ovarian cancer. Methods: We retrospectively reviewed the medical records from hospitals. Patients with epithelial ovarian cancer treated with olaparib or niraparib as frontline maintenance treatment between 1 January 2014 and 31 December 2022 were included. Progression-free survival (PFS) was analyzed using the Kaplan–Meier method, and adverse events associated with PARP inhibitor treatment were investigated. Results: Ninety-six patients treated with PARP inhibitors were identified. The median follow-up period was 21.8 months (95% confidence interval [CI] 19.4–24.0). Twenty (20.1%) patients experienced disease progression, and two patients died. The median PFS was 45.3 months (95% CI 39.4–NA). BRCA1 or BRCA2 gene mutations and primary cytoreductive surgery were associated with better PFS. Adverse events of any grade occurred in 74 (77.1%) patients. Nineteen (19.8%) patients experienced PARP inhibitor therapy interruptions, and 35 (36.5%) patients experienced dose reductions. Only three patients discontinued the drug due to adverse events. Conclusions: In a real-world setting, PARP inhibitors showed efficacy comparable to that reported in published randomized controlled trials and had acceptable safety profiles.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".