Evidence mapPaperPMID 42449855Full record

ReviewDiagnostics (Basel, Switzerland)2026

Preeclampsia Screening.

Yunyu Chen, Liona C Poon

Abstract readReview
In one paragraph

Review in Diagnostics (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Yunyu ChenDepartment of Obstetrics and Gynaecology, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong SAR, China.ORCID 0000-0002-5632-3445
Liona C PoonDepartment of Obstetrics and Gynaecology, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong SAR, China.ORCID 0000-0002-3944-4130

Funding

Croucher Senior Medical Research Fellowship awarded to L.C.P. None
6 · The paper itself

Abstract

Preeclampsia is a leading cause of maternal and perinatal morbidity and mortality worldwide. This significant burden necessitates effective early identification of pregnancies at high-risk for preeclampsia. Accurate prediction is essential in order to develop and optimize preventive strategies. The evolution of preeclampsia screening has progressed from a traditional checklist-based approach to individualized, multivariable models. The first-trimester triple test, which was developed by the Fetal Medicine Foundation (FMF), represents this advancement. It utilizes Bayes' theorem to calculate patient-specific risks by integrating maternal factors, mean arterial pressure, uterine artery pulsatility index, and serum placental growth factor. This model, called "first trimester FMF triple test", has undergone successful internal and external validation for the prediction of preterm preeclampsia. To ensure the reliability of biomarker measurements and achieve an optimal screening performance, it is essential to implement standardized measurement protocols and rigorous quality control processes in biomarker testing. The triple test could also be utilized in the 2nd and 3rd trimester, and the addition of biomarkers such as soluble fms-like tyrosine kinase-1 further improves risk stratification assessment and continued surveillance of high-risk pregnancies.

Indexed as

first trimesterpredictionpreeclampsiascreeningsecond trimesterthird trimester

Identifiers

PMID42449855
PMCPMC13361505

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.