Efficacy and safety of rolapitant for prevention of chemotherapy-induced nausea and vomiting (CINV) over multiple cycles of highly or moderately emetogenic chemotherapy (HEC, MEC).
Bibliographic record
Abstract
210 Background: The long-acting neurokinin-1 receptor antagonist (NK-1 RA) rolapitant has demonstrated efficacy for CINV prevention in patients receiving HEC and MEC during Cycle 1. The efficacy and safety of rolapitant was examined during subsequent cycles 2–6 in a pooled analysis. Methods: In 4 double-blind, active-controlled studies, patients were randomized to oral rolapitant 180 mg or placebo 1–2 hours before chemotherapy. All patients received active control: 5HT3 receptor antagonist + oral dexamethasone. Patients completing Cycle 1 could receive the same anti-emetic treatment in subsequent cycles. On Days 6-8 of subsequent cycles, patients self-reported the incidence of emesis, or of nausea interfering with normal daily life following Day 1 of chemotherapy. Results: A greater proportion of patients on rolapitant than on active control reported no emesis or interfering nausea separately for each subsequent cycle. Results of individual studies and pooled analysis are shown in the Table. During cycles 2-6, the incidence of treatment-related adverse events (AEs) was similar for rolapitant (5.5%) and control (6.8%). The most common treatment-related AEs were similar in both arms: constipation (rolapitant: 1.2%; control: 0.8%) and fatigue (rolapitant: 1.3%; control: 1.8%). Conclusions: Rolapitant was superior to active control in reducing CINV when administered over multiple cycles of moderately or highly emetogenic chemotherapy, with no increase in toxicity. Clinical trial information: NCT00394966 - NCT01500213 - NCT01500226 - NCT01499849. [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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".