Computational Approaches for Connecting Maternal Stress to Preterm Birth.

Amin Mirzaei, Bjarne C Hiller, Ina A Stelzer, Kristin Thiele, Yuqi Tan, Martin Becker

Abstract readReview

In one paragraph

Review in Clinics in perinatology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing 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

1 citing paper in PubMed.

Machine learning for the prediction of spontaneous preterm birth using early second and third trimester maternal blood gene expression: A cautionary tale.PloS one · 2025
Article

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

6 authors.

Amin MirzaeiDepartment of Computer Science and Electrical Engineering, Institute for Visual and Analytic Computing, Universität Rostock, Albert-Einstein-Straße 22, 18059 Rostock, Germany.

Bjarne C HillerDepartment of Computer Science and Electrical Engineering, Institute for Visual and Analytic Computing, Universität Rostock, Albert-Einstein-Straße 22, 18059 Rostock, Germany.

Ina A StelzerDepartment of Pathology, University of California San Diego, GPL/CMM-West, 9500 Gilman Drive, La Jolla, CA 92093, USA.

Kristin ThieleDivision for Experimental Feto-Maternal Medicine, Department of Obstetrics and Fetal Medicine, University Medical Center Hamburg-Eppendorf, Center for Obstetrics and Pediatrics, Martinistrasse 52, 20246 Hamburg, Germany.

Yuqi TanDepartment of Microbiology and Immunology, Stanford University School of Medicine, CSSR3220, 269 Campus Drive, Stanford, CA 94305, USA.

Martin BeckerDepartment of Computer Science and Electrical Engineering, Institute for Visual and Analytic Computing, Universität Rostock, Albert-Einstein-Straße 22, 18059 Rostock, Germany. Electronic address: martin.becker@uni-rostock.de.

Funding

Neuromodulation of maternal immune adaptations in pregnancyR00HD105016 · NICHD · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI STELZER, INA · 2023 to 2025

$745k

NICHD NIH HHS R00 HD105016

6 · The paper itself

Abstract

Multiple studies have hinted at a complex connection between maternal stress and preterm birth (PTB). This article describes the potential of computational methods to provide new insights into this relationship. For this, we outline existing approaches for stress assessments and various data modalities available for profiling stress responses, and review studies that sought either to establish a connection between stress and PTB or to predict PTB based on stress-related factors. Finally, we summarize the challenges of computational methods, highlighting potential future research directions within this field.

Indexed as

Premature BirthStress, PsychologicalFemaleHumansInfant, NewbornPregnancyArtificial intelligenceMachine learningPreterm birthStress

Identifiers

PMID38705645

DOI10.1016/j.clp.2024.02.003

PMCPMC12327838

What Socratic holds

Textmetadata

LicenceTDM

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.