Efficacy and tolerability of aprepitant for the prevention of chemotherapy‐induced nausea and emesis over multiple cycles of moderately emetogenic chemotherapy
Bibliographic record
Abstract
BACKGROUND: An aprepitant (APR) regimen was evaluated for prevention of nausea and emesis due to moderately emetogenic chemotherapy (MEC) over multiple cycles. METHODS: The authors performed a randomized, double-blind study. Eligible patients with breast carcinoma were naïve to emetogenic chemotherapy and treated with cyclophosphamide alone or with doxorubicin or epirubicin. Patients were randomized to receive either an APR regimen (Day 1: APR 125 mg, ondansetron [OND] 8 mg, and dexamethasone [DEX] 12 mg before chemotherapy and OND 8 mg 8 hrs later; Days 2-3: APR 80 mg every day) or a control regimen (Day 1: OND 8 mg and DEX 20 mg before chemotherapy and OND 8 mg 8 hrs later; Days 2-3: OND 8 mg twice per day). Data on nausea, emesis, and use of rescue medication were collected. The primary end point was the proportion of patients with a complete response (CR; no emesis or use of rescue therapy) in Cycle 1. Efficacy end points for the multiple-cycle extension were the probabilities of a CR in Cycles 2-4 and a sustained CR rate across multiple cycles. RESULTS: Of 866 patients randomized, 744 (85.9%) entered the multiple-cycle extension, and 650 (75.1%) completed all 4 cycles. Overall, the CR was greater with the APR regimen over the 4 cycles: 53.8% versus 39.4% for Cycle 2, 54.1% versus 39.3% for Cycle 3, and 55.0% versus 38.4% for Cycle 4. The cumulative percentage of patients with a sustained CR over all 4 cycles was greater with the APR regimen (P = 0.017). CONCLUSIONS: The APR regimen was more effective than a control regimen for the prevention of nausea and emesis induced by MEC over multiple chemotherapy cycles.
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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".