Evidence map›Paper›PMID 38705648›Full record

ReviewClinics in perinatology2024

Predicting Preterm Birth Using Proteomics.

Ivana Marić, David K Stevenson, Nima Aghaeepour, Brice Gaudillière, Ronald J Wong, Martin S Angst

Abstract readReview
PubMed Full text at PMC Publisher
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 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
  4. Article
  5. Article
  6. Review
  7. Article
  8. Review
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.

Ivana MarićDivision of Neonatal and Developmental Medicine, Department of Pediatrics, Stanford University School of Medicine, 453 Quarry Road, Palo Alto, CA 94304, USA. Electronic address: ivanam@stanford.edu.
David K StevensonDivision of Neonatal and Developmental Medicine, Department of Pediatrics, Stanford University School of Medicine, 453 Quarry Road, Palo Alto, CA 94304, USA.
Nima AghaeepourDepartment of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Grant Building, Office 276A, 300 Pasteur Drive, Stanford, CA 94305-5117, USA; Division of Neonatal and Developmental Medicine, Department of Pediatrics, Stanford University School of Medicine, 300 Pasteur Drive, Grant S280, Stanford, CA 94305, USA.
Brice GaudillièreDepartment of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Grant Building, Office 276A, 300 Pasteur Drive, Stanford, CA 94305-5117, USA; Division of Neonatal and Developmental Medicine, Department of Pediatrics, Stanford University School of Medicine, 300 Pasteur Drive, Grant S280, Stanford, CA 94305, USA.
Ronald J WongDivision of Neonatal and Developmental Medicine, Department of Pediatrics, Stanford University School of Medicine, 453 Quarry Road, Palo Alto, CA 94304, USA.
Martin S AngstDepartment of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Grant Building, Office 276A, 300 Pasteur Drive, Stanford, CA 94305-5117, USA.

Funding

Machine Learning for Integrative Modeling of the Immune System in Clinical SettingsR35GM138353 · NIGMS · STANFORD UNIVERSITY · PI AGHAEEPOUR, NIMA · 2020 to 2024
$2.2M
NIGMS NIH HHS R35 GM138353
6 · The paper itself

Abstract

The complexity of preterm birth (PTB), both spontaneous and medically indicated, and its various etiologies and associated risk factors pose a significant challenge for developing tools to accurately predict risk. This review focuses on the discovery of proteomics signatures that might be useful for predicting spontaneous PTB or preeclampsia, which often results in PTB. We describe methods for proteomics analyses, proteomics biomarker candidates that have so far been identified, obstacles for discovering biomarkers that are sufficiently accurate for clinical use, and the derivation of composite signatures including clinical parameters to increase predictive power.

Indexed as

BiomarkersPremature BirthProteomicsFemaleHumansInfant, NewbornPredictive Value of TestsPre-EclampsiaPregnancyBiomarkersBiomarkersBiosignaturesComposite signaturesComputationMultiomicsOmicsPreeclampsiaProteome

Identifiers

PMID38705648
PMCPMC11186213

What OpenQuestion 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 OpenQuestion graph.