A global perspective of correlation between maternal blood lead levels and risks of preeclampsia: An updated systematic review and meta-analysis
Bibliographic record
Abstract
Background Preeclampsia (PE) is a specific hypertensive disorder in pregnancy. Lead (Pb) is a heavy metal that affects women's reproductive health. However, it is unclear whether lead exposure during can predispose maternal risk of developing preeclampsia. This systematic review and meta-analysis study aimed to explore the association. Methods We searched studies from three databases (PubMed, Web of Science, Embase). Only case-control, cross-sectional, and cohort studies reporting maternal blood lead levels (BLL) and PE were included from database inception to 31st July 2022. Pregnant women with blood lead levels measured were eligible. Those healthy pregnant women who did not develop preeclampsia were assessed as comparators. Letters, comments, case reports, and reviews were excluded. Newcastle-Ottawa Scale (NOS) and its adaptive form were applied for assessment. The random-effects method (REM) was applied to calculate the standardized mean difference (SMD) with a 95% confidence interval (CI). Stata 16.0 and RevMan 5.3 were the software used for data extraction and analysis. Results 25 studies out of 1,808 articles made the finalist for systematic reviews, of which 21 underwent further quantity analysis. A total of 1,533 preeclamptic women and 10,998 healthy pregnant controls were included in the meta-analysis. The overall result revealed that maternal lead exposure was significantly higher in women with preeclampsia (SMD: 1.06, 95% CI 0.69, 1.43); ( I 2 = 96.40%; P = 0.000). Conclusion This study demonstrates that maternal lead exposure is associated with preeclampsia during pregnancy. The association is present even in low blood lead levels. The conclusion should be taken seriously and women should avoid unexpected exposure to a lead-containing environment as much as possible. Systematic review registration https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=347220 , identifier: CRD42022347220.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| 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) | 0.000 | 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 teacher head, 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".