Efficacy and safety of rolapitant for prevention of chemotherapy-induced nausea and vomiting over multiple cycles of moderately or highly emetogenic chemotherapy
Bibliographic record
Abstract
OBJECTIVE: Rolapitant, a novel neurokinin-1 receptor antagonist (RA), was shown to protect against delayed chemotherapy-induced nausea and vomiting (CINV) during the first cycle of moderately emetogenic chemotherapy (MEC) or highly emetogenic chemotherapy (HEC) in randomized, double-blind trials. This analysis explored the efficacy and safety of rolapitant in preventing CINV over multiple cycles of MEC or HEC. PATIENTS AND METHODS: Patients in one phase III MEC, one phase II HEC, and two phase III HEC clinical trials were randomized to receive oral rolapitant (180 mg) or placebo in combination with a 5-hydroxytryptamine type 3 RA and dexamethasone. Regardless of response in cycle 1, patients could continue the same antiemetic treatment for up to six cycles. On days 6-8 of each subsequent chemotherapy cycle, patients reported the incidence of emesis and/or nausea interfering with normal daily life. Post hoc analyses of pooled safety and efficacy data from the four trials were performed for cycles 2-6. RESULTS: Significantly more patients receiving rolapitant than control reported no emesis or interfering nausea (combined measure) in cycles 2 (p = 0.006), 3 (p < 0.001), 4 (p = 0.001), and 5 (p = 0.021). Over cycles 1-6, time-to-first emesis was significantly longer with rolapitant than with control (p < 0.001). The incidence of treatment-related adverse events during cycles 2-6 was similar in rolapitant (5.5%) and control (6.8%) arms. No cumulative toxicity was observed. CONCLUSIONS: Over multiple cycles of MEC or HEC, rolapitant provided superior CINV protection and reduced emesis and nausea interfering with daily life compared with control and remained well tolerated.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".