Rolapitant improves quality of life of patients receiving highly or moderately emetogenic chemotherapy
Bibliographic record
Abstract
PURPOSE: Addition of rolapitant to standard antiemetic therapy improved protection against chemotherapy-induced nausea and vomiting (CINV) in phase 3 trials of patients receiving highly emetogenic chemotherapy (HEC) or moderately emetogenic chemotherapy (MEC). Here, we assessed the impact of CINV on the daily lives of patients receiving HEC or MEC using the Functional Living Index-Emesis (FLIE). METHODS: In three double-blind phase 3 studies, patients receiving HEC or MEC were randomized 1:1 to receive oral rolapitant 180 mg or placebo prior to chemotherapy plus 5-hydroxytryptamine type 3 receptor antagonist and dexamethasone therapy. Patients completed the FLIE questionnaire on day 6 of cycle 1. Endpoints included FLIE total score, nausea and vomiting domain scores, and the proportion of patients with no impact on daily life (total score >108 [range 18-126]). We performed a prespecified analysis of the MEC/anthracycline-cyclophosphamide (AC) study and a post hoc analysis of two pooled cisplatin-based HEC studies. RESULTS: In the pooled HEC studies, rolapitant significantly improved the FLIE total score (114.5 vs 109.3, p < 0.001), nausea score (55.3 vs 53.5, p < 0.05), and vomiting score (59.2 vs 55.8, p < 0.001) versus control; similar results were observed in the MEC/AC study for FLIE total score (112.7 vs 108.6, p < 0.001), nausea score (54.1 vs 52.3, p < 0.05), and vomiting score (58.6 vs 56.3, p < 0.001). A higher proportion of patients reported no impact on daily life with rolapitant than with control in the MEC/AC study (73.2 vs 67.4, p = 0.027). CONCLUSIONS: Compared with control, rolapitant improved quality of life in patients receiving HEC or MEC.
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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) | 0.001 | 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".