A randomized trial of individualized versus standard of care antiemetic therapy for breast cancer patients at high risk for chemotherapy-induced nausea and vomiting
Bibliographic record
Abstract
PURPOSE: Despite triple antiemetic therapy use for breast cancer patients receiving emetogenic chemotherapy, nausea remains a clinical challenge. We evaluated adding olanzapine (5 mg) to triple therapy on nausea control in patients at high personal risk of chemotherapy-induced nausea and vomiting (CINV). METHODS: This multi-centre, placebo-controlled, double-blind trial randomized breast cancer patients scheduled to receive neo/adjuvant chemotherapy with anthracycline-cyclophosphamide or platinum-based chemotherapy to olanzapine (5 mg, days 1-4) or placebo. Primary endpoint was frequency of self-reported significant nausea, repeated for all cycles of chemotherapy. Secondary endpoints included: duration of nausea, overall total control of CINV, Health Related Quality of Life (HRQoL) using FLIE questionnaire, use of rescue mediation and treatment-related adverse events. RESULTS: 218 eligible patients were randomised to placebo (105) or olanzapine (113). From days 0-5 following each cycle of chemotherapy, 41.3% (95%CI: 36.1-46.7%) of patients in the placebo group reported significant nausea compared to 27.7% (95%CI: 23.2-32.4%) in the olanzapine group (p = 0.001). Across all cycles of chemotherapy, patients receiving olanzapine experienced a statistically significant improvement in HRQoL (p < 0.001). Grade 1/2 sedation was the most commonly side effect reported at 40.8% in the placebo group vs. 54.1% with olanzapine (p < 0.001). CONCLUSION: In patients at high personal risk of CINV, the addition of olanzapine 5 mg daily to standard antiemetic therapy significantly improves the control of nausea, HRQoL, with no unexpected toxicities.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".