Risk predictors of severe adverse maternal outcomes in pre-eclampsia: a systematic review and meta-analysis protocol
Bibliographic record
Abstract
INTRODUCTION: Pre-eclampsia (PE) remains a major contributor to maternal morbidity and mortality globally. Early identification of risk factors and evaluation of prognostic models for severe adverse maternal outcomes are essential for improving management and reducing complications. While numerous studies have explored potential risk markers, there is still no consensus on the most reliable factors and models to use in clinical practice. This systematic review aims to consolidate research on both individual predictors and prognostic models of severe adverse maternal outcomes in PE, providing a comprehensive overview to support better clinical decision-making and patient care. METHODS AND ANALYSIS: This review follows the Meta-analyses Of Observational Studies in Epidemiology (MOOSE) guidelines and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) Protocol 2015 checklist. A systematic search will be performed using a detailed strategy across Medline, Embase, Cochrane, ProQuest dissertations, and grey literature from inception to 2 April 2024. Eligible studies will include those investigating clinical, laboratory-based, and sociodemographic predictors of severe adverse maternal outcomes in PE. Two reviewers will independently assess titles, abstracts, full texts, and extract data and assess study quality using the Quality In Prognostic Studies (QUIPS) tool for studies on risk predictors and the Prediction model Risk of Bias Assessment Tool (PROBAST) for prognostic models. The inclusion criteria will encompass cohort, case-control, and cross-sectional studies published in English and French involving women diagnosed with PE and reporting on the risk prediction for adverse maternal outcomes. The main outcomes of interest will include severe maternal morbidity and mortality during pregnancy, delivery, or within the postpartum period. Analyses will include both narrative synthesis and, where appropriate, meta-analysis using random-effects models. Pooled estimates will be calculated, with publication bias assessed through funnel plots and statistical tests (eg, Begg's and Egger's). Heterogeneity will be primarily assessed through visual inspection of forest plots, supported by statistical measures, such as the I² test, with further exploration through sensitivity, subgroup, and meta-regression analyses. ETHICS AND DISSEMINATION: This systematic review will be based on published data and will not require ethics approval. Results will be disseminated through peer-reviewed publications and presentations at academic conferences. PROSPERO REGISTRATION NUMBER: CRD42024517097.
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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 | 0.055 | 0.083 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.019 | 0.021 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.089 | 0.008 |
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".