Olanzapine for the Prevention and Treatment of Chemotherapy-Induced Nausea and Vomiting: A Review to Identify the Best Way to Administer the Drug
Bibliographic record
Abstract
Common treatment methods for malignant tumors include surgery, chemotherapy, radiotherapy, immunotherapy, targeted therapy, etc., among which chemotherapy plays an important role. However, chemotherapy brings corresponding side effects while killing tumor cells, and nausea and vomiting are the most common adverse reactions induced by chemotherapy. It not only affects the patient's appetite, resulting in malnutrition and electrolyte disturbances, but also reduces the patient's compliance with treatment, which further aggravates the disease. Thus, it is important to quickly prevent and cure nausea and vomiting induced by chemotherapy (CINV). In addition, with the continuous development of medicine, more and more antiemetic drugs have been developed. At present, the most common antiemetic agents for chemotherapy-induced nausea and vomiting are NK-1R antagonists, 5-HT3R antagonists, and dexamethasone. Surprisingly, olanzapine, often used as a psychotropic drug, has been found to be an effective antiemetic and is similar to other regimens on the safety of medicine. However, although there are numerous studies on the antiemetic effects of olanzapine, its comprehensive application remains unclear. Therefore, this review will elaborate the antiemetic effect of olanzapine in terms of the antiemetic mechanism and the safety, economic cost, dose, administration time, and drug delivery aspects.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".