Effect of Ginger versus Antiemetics on Relieving Mild to Moderate Morning Sickness among Pregnant Women
Bibliographic record
Abstract
Background: Nausea and vomiting during pregnancy (NVP) is the first and most frequently reported minor discomforts during early pregnancy. Ginger is alternative therapy used for pregnant women with NVP. Aim of the study: The aim of the study was to evaluate the effect of ginger versus antiemetics on relieving mild to moderate morning sickness among pregnant women. Design: quasi – experimental design was adopted in this study. Setting: the study was carried out at the obstetrics outpatient's clinic in Suez Canal University hospital in Ismailia city. Sample: Purposive sampling was used to recruit 100 pregnant women have NVP during the first trimester of pregnancy. 50 women were assigned into study group (those who undergo use ginger syrup 1 gm per day for four consecutive days), and other 50 women were assigned as control group. Tools and procedure: Structure interviewing schedule, and McGill Nausea Questionnaire, which used to assess patterns of NVP. Results: The frequency and severity of nausea and vomiting have shown statistically significant improvement throughout the following up in the study and control group. Improvement in the overall symptoms was more revealed in the study group than the control groupPConclusion: Ginger drink as well as antiemetic agent is effective in relieving mild to moderate nausea and vomiting during the first trimester of pregnancy. Recommendation: Using ginger as antiemetic agent on relieving mild to moderate nausea and vomiting during the first trimester of pregnancy is recommended.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".