Effect of Ginger Tea on Chemotherapy-Induced Nausea and Vomiting among Patients Attending the Oncology Teaching Hospital, Baghdad 2020
Bibliographic record
Abstract
Background: Ginger has been widely used to relieve nausea and vomiting in several settings, one ofthem, patients receiving chemotherapy. This study was done to assess the effect of ginger in controlling thechemotherapy induced nausea and vomiting (CINV) among patients. Methods: An interventional (pre-post)study design was conducted in oncology teaching hospital in Baghdad for three months. Sixty participantswere randomly assigned into intervention group (30 participants received ginger tea (1.5 g/d) with routineantiemetic regimen for the first 5 days of the chemotherapy cycle) and control group (30 participants receivedonly routine antiemetic regimen). MASCC Antiemesis Tool (MAT) was used for assessment of CINV incancer patients before and after the use of ginger tea.Results: No significant difference was observed between the intervention and control groups in the acuteand delayed phases of CINV after intervention with ginger tea(p >0.05), but difference between the studygroups was found statistically significant (p <0.05)regarding the severity of nausea postchemotherapy.Conclusions: The addition of ginger tea to routine antiemetic regimen in patients receiving chemotherapyeffectively reduced the severity of nausea. However, there is no additional role for ginger in reducing theacute and delayed phases of CINV.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".