Towards the Development of a Conceptual Framework of the Determinants of Pre‐eclampsia: A Hierarchical Systematic Review of Biomarkers
Bibliographic record
Abstract
BACKGROUND: Pre-eclampsia is a leading cause of maternal and perinatal morbidity and mortality. There are several determinants of individual pregnant women's risk of developing pre-eclampsia, including biomarkers and ultrasound markers. OBJECTIVE: A conceptual framework to collate and summarise the extensive body of literature on biomarkers (including ultrasound markers) associated with pre-eclampsia, through a hierarchical systematic literature review. SEARCH STRATEGY: Medline, Embase, Health Technology Assessments, Database of Abstracts of Reviews of Effects, Cochrane Library were searched until April 2024. SELECTION CRITERIA: Reviews and cohort studies (> 100 participants) reporting biomarkers associated with pre-eclampsia were included. DATA COLLECTION AND ANALYSIS: Studies were screened by title, then abstract and full text. Evidence was prioritised from umbrella reviews, followed by systematic reviews and then observational studies. Associations were assessed for strength of association and quality of evidence using GRADE. MAIN RESULTS: The biomarker domain included 40 individual determinants of pre-eclampsia. Of these, there were 18 biomarkers with definite or probable associations based on moderate-strong quality evidence across markers of angiogenic imbalance, fetal-placental unit function, inflammatory and immune markers, and physiological markers. Vascular endothelial growth factor, human chorionic gonadotropin, inhibin-A, maternal serum placental protein-13, and interferon-gamma had definite associations based on high-quality evidence. CONCLUSION: Biomarkers associated with the development of pre-eclampsia highlight the multi-factorial aetiology of the syndrome. The addition of biomarkers, including ultrasound, will optimise the prediction of pre-eclampsia and enable individualised risk stratification.
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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 | 0.104 | 0.176 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.013 |
| Bibliometrics | 0.044 | 0.029 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.015 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".