Pre-Pregnancy Body Mass Index and the Risk of Hyperemesis Gravidarum: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Hyperemesis gravidarum (HG) is an infrequent and severe occurrence of nausea and/or vomiting during pregnancy, with a prevalence ranging from 0.3% to 2%. Until now, no meta-analytic study has been undertaken to assess the correlation between pre-pregnancy body mass index (BMI) and the likelihood of HG. Consequently, this meta-analysis was carried out to examine the connection between BMI and HG risk. Methods: For this systematic review and meta-analysis, we conducted a thorough search of electronic bibliographic databases such as PubMed, Web of Science, Scopus, and Science Direct until May 2022. The outcomes were presented utilizing a random-effects model. Heterogeneity was assessed using the chi-square test and I2 statistic. Potential publication bias was examined using Begg’s test. Additionally, we evaluated the quality of studies using the Newcastle Ottawa Scale. Results: In total, seven studies were included in the present meta-analysis such as six cohort studies and one cross-sectional study. In this meta-analysis, 3,573,663 participants were involved. Based on the results, the underweight was a risk factor for HG (odds ratio (OR) = 1.91, 95% confidence interval (CI): 1.21, 2.61). There was not significant association between overweigh and HG (OR = 1.23, 95% CI: 0.96, 1.50). In addition, there was not significant association between obesity and HG (OR = 0.88, 95% CI: 0.42, 1.34). Heterogeneity was seen among the included studies. Conclusions: An apparent association between pre-pregnancy BMI and the risk of HG has been observed. However, further investigation is necessary, as the mechanisms and the connection to GDF15 are not yet clear. The most supported public health advice is to attain a healthy BMI before conception. Additionally, the oversight of confounding variables should be taken into account, highlighting the need for comprehensive consideration in future studies.
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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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.045 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".