Optimizing Antiemetic Support in Anthracycline-Based Chemotherapy for Early Breast Cancer: Protocol for a Prospective Observational Study of Four-Drug Antiemetic Therapy Including Fosnetupitant and Olanzapine
Bibliographic record
Abstract
Background: Prophylaxis for chemotherapy-induced nausea and vomiting (CINV) in patients receiving highly emetogenic chemotherapy (HEC) is essential. Four-drug antiemetic therapy, consisting of a neurokinin-1 receptor antagonist (NK1RA), a 5-hydroxytryptamine type 3 receptor antagonist (5-HT3RA), dexamethasone (DEX), and olanzapine (OLZ), is currently recommended for HEC. However, the efficacy, optimal dosing schedule, and appropriate dosage of OLZ remain unclear when combined with a highly selective NK1RA, fosnetupitant (FosNTP). Objective: This study aimed to evaluate the efficacy and safety of a four-drug antiemetic regimen including FosNTP and OLZ for prophylaxis of CINV in patients receiving anthracycline-based (neo)adjuvant chemotherapy for early breast cancer (EBC) and to explore outcomes with OLZ dose adjustments in the second cycle. Methods: This single-institution, prospective observational study will enroll 100 patients with EBC who undergo HEC. All patients will receive a four-drug antiemetic regimen consisting of FosNTP, 5-HT3RA, DEX, and the guideline-recommended dose of OLZ administered orally from days -1 to 4. During the second cycle of treatment, OLZ dosing may be adjusted based on tolerability and patient preference. The primary endpoint is the proportion of patients who experienced no nausea during the overall phase (0-120 h) of the first cycle, as assessed using the daily visual analog scale. A sample size of 86 was calculated to assess the efficacy of the four-drug antiemetic regimen, including OLZ, assuming an expected no nausea rate of 42%, a threshold no nausea rate of 27% based on historical data, 90% power, and a 1-sided significance level of 5.0%. A single-sample z-test with a normal approximation will be used for the analyses. Results: This study was approved by the Ethics Committee of the National Hospital Organization Kyushu Cancer Center, and participant recruitment and data collection commenced in May 2024. As of September 2025, approximately 70 participants have been recruited. Data collection is expected to continue until April 2026, and both data collection and analyses are anticipated to be completed in 2027. Conclusions: This study will provide real-world evidence on the effectiveness and safety of a four-drug antiemetic regimen, including FosNTP and OLZ, in patients receiving anthracycline-based HEC and may inform optimal OLZ dosing strategies.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".