Evidence map›Paper›PMID 42800814›Full record

ArticleJournal of perinatology : official journal of the California Perinatal Association2026

Impact of SGA, AGA, and LGA birthweight subclassification on NICU mortality risk identification: a cohort study.

A Nicole Ferguson, Joseph R Stanton, Brandi D Jones, Irene E Olsen, Reese H Clark, Joe Bible, Matthew Reith, Jessica G Woo

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Article in Journal of perinatology : official journal of the California Perinatal Association, 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
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0citing papers in PubMed
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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

8 authors.

A Nicole FergusonSchool of Data Science and Analytics, Kennesaw State University, Kennesaw, GA, USA.ORCID http://orcid.org/0000-0003-0696-185X
Joseph R StantonSchool of Data Science and Analytics, Kennesaw State University, Kennesaw, GA, USA.
Brandi D JonesSchool of Data Science and Analytics, Kennesaw State University, Kennesaw, GA, USA.ORCID http://orcid.org/0009-0001-7568-3251
Irene E OlsenNutrition Sciences Department, College of Nursing and Health Professions, Drexel University, Philadelphia, PA, USA.
Reese H ClarkDuke University Medical Center, Department of Pediatrics, Durham, NC, USA.ORCID http://orcid.org/0000-0002-7363-4640
Joe BibleSchool of Mathematical and Statistical Sciences, Clemson University, Clemson, SC, USA.ORCID http://orcid.org/0000-0002-7010-5321
Matthew ReithSchool of Data Science and Analytics, Kennesaw State University, Kennesaw, GA, USA.ORCID http://orcid.org/0009-0008-8510-0727
Jessica G WooDepartment of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA. Jessica.woo@cchmc.org.ORCID http://orcid.org/0000-0003-3644-8432

Funding

Gerber Foundation 7247
6 · The paper itself

Abstract

objectiveTo assess whether subdividing small (SGA, <10th), appropriate (AGA, 10th-90th), and large (LGA, >90th percentile) for gestational age (GA) birthweight classifications improves mortality risk estimation among preterm infants in neonatal intensive care units (NICUs). STUDY

designData from 25,779 singleton inborn NICU infants (24-30 weeks GA) from the Pediatrix Clinical Data Warehouse (2013-2018) were analyzed. Infant birthweights were subclassified into 12 subgroups (extremely SGA [eSGA], <3rd; moderately SGA [mSGA], 3rd-<10th, 8 AGA categories [3-AGA to 9-AGA]; mLGA, >90th-97th; eLGA, >97th percentile). Mortality probability by birthweight category was modeled using logistic regression.

resultMortality probability was higher for infants classified as eSGA (28.9%, 95% CI [26.4-31.4%]) than mSGA (9.9% [8.7-11.3%]). Smaller AGA infants had higher mortality than larger AGA infants (e.g., 3-AGA: 8.5% [7.5-9.7%] vs. 9-AGA: 5.0% [4.2-5.9%]). Subdividing LGA provided no additional information.

conclusionSubdivision of SGA and AGA birthweight categories may improve mortality risk prediction among premature infants.

Identifiers

PMID42800814

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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.