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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 teacher head, 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".