Ginger Relief Chemotherapy Induced Nausea and Vomiting (CINV) in Children: A Randomized Clinical Trial
Bibliographic record
Abstract
Background One of the major adverse effects of chemotherapy is chemotherapy induced nausea and vomiting (CINV) which can obviously reduce patients’ quality of life. Ginger (Zingiber officinale), an herbal supplement, has been used for centuries for gastrointestinal complaints. Although many surveys have been conducted to find the efficiency of ginger on CINV, its benefit has not been proven yet. We aimed to find ginger’s efficiency on pediatric patients throughout their chemotherapy cycles. Materials and Methods: This was a double-blinded, randomized, single institutional, placebo-controlled trial conducted at oncology ward in Aliasghar children’s hospital, Tehran, Iran. The study took place between October 2017 and October 2018. We included 49 chemotherapy cycles, 25 cycles for treatment group and 24 cycles for placebo groups. Intervention group took encapsulated ginger which contained 240mg of powdered ginger (Nausophar), and control group took placebo. All patients took the study medication four times per day (every 6h), starting on the first day of chemotherapy until 24h after completion of chemotherapy. Frequency and severity of nausea and vomiting were measured by Edmonton’s Symptom Assessment Scale (ESAS) from the first day of chemotherapy until 24h after completion of chemotherapy. Results: The median age of all participants was 13 (IQR=8-14 year-old). Fourteen patients were male (28.6%), and 35 patients were female (71.4%). There were no significant differences in distribution of patients’ characteristics in two groups. The frequency and severity of nausea and vomiting were significantly lower in ginger group (p <0.05). Conclusion According to our findings, ginger acts as an efficient antiemetic for pediatric patients. We recommend that ginger be prescribed as well as other antiemetics like Granisetron, with no loss of function.
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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) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.009 | 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".