Maternal Body Mass Index and the Risk of Preeclampsia: A Systematic Overview
Bibliographic record
Abstract
Background. Maternal obesity, both in itself and as part of the insulin resistance syndrome, is an important risk factor for the development of preeclampsia. Accurately quantifying the relation between prepregnancy maternal body mass index and the risk of preeclampsia may better identify those at highest risk. We performed a systematic overview of the literature to determine the association between prepregnancy body mass index and the risk of preeclampsia. Methods. Two reviewers independently retrieved all relevant English language cohort studies through a systematic search of Medline and Embase between 1980 and June 2002. Study data were abstracted in a similar fashion. For each study, the risk ratio of preeclampsia was calculated by comparing the risk of preeclampsia among women with the highest body mass index with those with the lowest. Results. We identified thirteen cohort studies, comprising nearly 1.4 million women. The risk of preeclampsia typically doubled with each 5–7 kg/m 2 increase in prepregnancy body mass index. This relation persisted in studies that excluded women with chronic hypertension, diabetes mellitus or multiple gestations, or after adjustment for other confounders. Conclusions. Most observational studies demonstrate a consistently strong positive association between maternal prepregnancy body mass index and the risk of preeclampsia. Increasing obesity in developed countries is likely to increase the occurrence of preeclampsia. Consideration should be given to the potential benefits of prepregnancy weight reduction programs.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".