Quantifying the global rates of nausea and vomiting of pregnancy: a meta analysis.
Bibliographic record
Abstract
BACKGROUND: Nausea and vomiting of pregnancy (NVP) is the most common medical condition in pregnancy, affecting women worldwide. It is unclear whether its prevalence and severity NVP are variable across different nations and races. PURPOSE: To summarize global rates of NVP as reported in the literature using meta-analysis. METHODS: We searched Medline, Embase and Cochrane databases for all peer-reviewed articles reporting rates of NVP and/or hyperemesis gravidarum (HG). No restrictions were imposed on publication year or language. Numbers of women, studies and NVP rates were extracted and aggregated using a random effects model. Outcomes included: overall rates (i.e., women suffering any nausea or vomiting or both) in early and in late pregnancy, rates of nausea only, symptom severity, and HG rates. RESULTS: We identified 116 studies, rejecting 37 and accepting 79, of which 59 provided data for NVP (N=93,753 in 13 countries) and 26 for HG (N= 6,155,578). All developed regions of the world were represented (2 studies from Africa, 1 India; none from Latin America). Reported NVP rates varied from 35%-91% (median 69%); the meta-analytic average rate was 69.4% (CI95%:66.5%-72.3%). Among pregnant women, 32.7% had nausea without vomiting and 23.5% overall had NVP continuing into the third trimester. NVP was rated as mild in 40%, moderate in 46% and severe in 14% of cases. The prevalence of HG was 1.1% (CI95%:0.8%-1.3%), with a range of 0.3%-3.6%. CONCLUSIONS: Almost 70% of women worldwide experience NVP, but reported rates vary widely. HG, the most severe form, affects 1.1%.
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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 | 0.036 | 0.064 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.081 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".