Time-Trend Analysis and Risk Factors for Niraparib-Induced Nausea and Vomiting in Ovarian Cancer: A Prospective Study
Bibliographic record
Abstract
PURPOSE: Nausea and vomiting are major non-hematological adverse events associated with niraparib maintenance therapy. This study aimed to investigate the time-trend patterns of niraparib-induced nausea and vomiting (NINV) and the associated risk factors in patients with ovarian cancer. Materials and Methods: In this prospective study, we enrolled patients with stage III-IV epithelial ovarian cancer who received niraparib as frontline maintenance therapy. The clinicopathological characteristics and time-trend patterns of patients with NINV were collected through in-person surveys and electronic medical records from the National Cancer Center. RESULTS: Of 53 patients, 50 (94.3%) were diagnosed with high-grade serous ovarian carcinoma. BRCA mutations and homologous recombination deficiency (HRD) were identifi ed in 23 (43.4%) and 32 (60.4%) patients, respectively. Thirty-one patients (58.5%) had NINV. Time-trend analyses revealed that the fi rst peak intensity of NINV was reached at 3 h post-dose, and the second peak intensity was reached at 11 hour post-dose. NINV signifi cantly decreased from week 1 to weeks 8 and 12. In multivariate analyses of risk factors for NINV, HRD-positive tumors (p < 0.001) and prior experience of chemotherapy-induced nausea and vomiting (p=0.004) were associated with the occurrence of NINV. CONCLUSION: Pre-emptive treatment with antiemetics is required to manage early-phase NINV during niraparib maintenance therapy in patients with risk factors. Additional larger studies are needed to confi rm these fi ndings and to develop optimal preventive strategies for NINV.
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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 | 0.001 |
| 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) | 0.001 | 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".