Factors associated with preeclampsia and the hypertensive disorders of pregnancy amongst Indigenous women of Canada, Australia, New Zealand, and the United States: A systematic review and meta-analysis
Bibliographic record
Abstract
PURPOSE OF THE REVIEW: Preeclampsia and the Hypertensive Disorders of Pregnancy (HDP) occur more frequently amongst Indigenous women and can have short- and long-term impacts on maternal and infant health and wellbeing. To understand factors associated with increased risk for Indigenous women a systematic review and meta-analysis was conducted. The PRISMA guidelines were adhered to, and the review protocol was registered on PROSPERO (Registration CRD42023381847). EndNote, Covidence and Excel were used to screen and extract data, with studies assessed using JBI critical appraisal tools. RECENT FINDINGS: Seven studies from Canada, Australia, and the United States (none from New Zealand) were included in this review. Meta-analysis showed women classified as overweight (OR 1.32, 95% CI: 1.09-1.60), obese (OR 1.88, 95% CI: 1.57-2.25), or having high mean BMI (MD 3.02 95% CI: 1.72-4.31), high mean systolic blood pressure (MD 15.19 95% CI: 12.83-17.541), or high mean diastolic blood pressure (MD 15.26 95% CI: 13.05-17.47), pre-pregnancy diabetes (OR 3.63, 95% CI: 1.66-17.94), or high microalbuminuria (OR 2.76, 95% CI: 1.40-5.43) were more likely to be diagnosed with preeclampsia. Smoking (OR 0.77, 95% CI: 0.58-1.03), alcohol consumption (OR 1.70, 95% CI: 0.76-3.81), and gestational diabetes (OR 1.74, 95% CI: 0.90-3.37) were not associated with preeclampsia. Understanding factors associated with increased preeclampsia/HDP risk amongst Indigenous women is important to minimising adverse perinatal events and future health complications. This review demonstrates current gaps in the evidence, specifically in relation to social, economic, and environmental factors.
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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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.026 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".