Randomized Phase II Trial to Compare the Efficacy of Haloperidol and Olanzapine in the Control of Chemotherapy-Induced Nausea and Vomiting in Nepal
Bibliographic record
Abstract
PURPOSE The purpose of the study was to compare efficacy and toxicity of olanzapine (OLN; a higher-cost drug) and haloperidol (HAL; a lower-cost drug) in the prevention of chemotherapy-induced nausea and vomiting (CINV) in patients who receive highly emetogenic chemotherapy (HEC). PATIENTS AND METHODS In a randomized, phase II trial, patients were randomly assigned to receive either OLN 10 mg orally on days 1 to 4 or HAL 1 mg orally on day 1 and 0.5 mg twice daily on days 2 to 4. Both groups received ondansetron 16 mg and dexamethasone 12 mg intravenously on day 1. Patients recorded their nausea using the Edmonton Symptom Assessment Scale (ESAS) and recorded daily episodes of vomiting from day 1 to day 5. The primary end point was complete nausea prevention (CNP; ie, ESAS of 0). Secondary end point was complete emesis prevention (CEP). RESULTS Sixty-five patients were randomly assigned, and 64 received their allocated treatment (n = 32 in each arm). There was no difference in CNP during the overall period (days 1 to 5) between OLN and HAL (68.7% v 71.8%; P = .78). In the acute period (day 1) and the delayed period (days 2 to 5), CNP was similar between OLN and HAL (acute: 84.3% v 81.2%; delayed: 68.7% v 75%). No difference was identified in the rate of CEP during the overall period (81.2% with OLN v 78.1% with HAL; P = .75), during the acute period (93.7% with OLN v 90.6% with HAL), or during the delayed period (84.3% with OLN v 84.3% with HAL). No difference in toxicities was noted between treatment arms. CONCLUSION In this study, HAL had comparable efficacy to OLN in the management of CINV, which suggests that it is the higher-value option in patients who receive HEC in resource-scarce countries.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".