ArticleSAGE open medicine2021
Risk assessment of preterm birth through identification and stratification of pregnancies using a real-time scoring algorithm.
Article in SAGE open medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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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.
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.
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Who cites it
5 citing papers in PubMed.
- "Actionable" risk for preterm birth: patterns and prediction in California singleton births 2016-2020.BMC pregnancy and childbirth · 2026Article
- Prediction of spontaneous preterm birth in pregnant women using machine learning.Archives of gynecology and obstetrics · 2025Article
- Predicting risk of preterm birth in singleton pregnancies using machine learning algorithms.Frontiers in big data · 2024Article
- Risk Scoring Systems for Preterm Birth and Their Performance: A Systematic Review.Journal of clinical medicine · 2023Review
- Impact of Technology on Neonatal Intensive Care Unit Admissions and Length of Stay: A Retrospective Study.Cureus · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionPreterm birth poses a significant challenge. This study evaluated a real-time scoring algorithm to identify and stratify pregnancies to indicate preterm birth.
methodsAll claims data of pregnant women were reviewed between 1 January 2014 and 31 October 2018 in Kentucky.
resultsA total of 29,166 unique women who were matched to a live newborn were documented, with the pregnancy identified during the first trimester in 54.1% of women. Negative predictive values, sensitivity, and positive likelihood ratios increased from the first to third trimesters as pregnant women who were matched to a live newborn had more visits with their physicians. The area under the receiving-operating characteristics curve on test data classifying preterm birth was 0.59 for pregnancies identified during the first trimester, 0.62 for pregnancies identified in the second trimester, and 0.73 for pregnancies identified in the third trimester.
conclusionsThis study presents a real-time scoring algorithm of indicating preterm birth in the first trimester of gestation which permits stratification of pregnancies to provide more efficient early care management.
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Registered trials
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