Prevalence of nausea and vomiting of pregnancy in the USA: a meta analysis.
Bibliographic record
Abstract
BACKGROUND: Nausea and vomiting of pregnancy (NVP) is the most common medical condition during gestation, carrying tremendous health burden, especially for the severe form, hyperemesis gravidarum (HG). The rates of NVP in the USA have not been systematically calculated. OBJECTIVES: To estimate the rates of NVP and HG in the USA. METHODS: A meta-analysis was conducted of all peer-reviewed articles from the USA that provided rates of NVP in early or late pregnancy or HG. Medline, Embase and Cochrane databases were searched from inception through November 2012; reviews and articles were hand searched. Rates were combined across studies using a random effects model. RESULTS: Forty-eight articles were identified; 15 were rejected and 33 were included for analysis. Twenty-three studies of 67,602 women provided rates of NVP which had a meta-analytic rate of 68.6% (CI95%:64.4%-72.8%). Three of them (N=5034) reported nausea without vomiting in 28.6% and two studies (N=136) produced a rate for NVP during late pregnancy of 24.0%. HG occurred in 1.2% of the 2.1 million women in 12 studies. CONCLUSIONS: We have summarized rates of NVP and HG, which are similar to those found in other parts of the world. Almost 70% of women suffer some form of the syndrome; 1.2% have the severe form, most of whom were hospitalized because of the HG. Future research should address issues of cost and resource utilization.
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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 | 0.020 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.060 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".