Development and validation of super learner models to predict small and large for gestational age in the second generation.

Mary M Brown, Stefan Kuhle, Bruce Smith, Victoria M Allen, Jennifer Payne, Christy G Woolcott

Abstract readValidation Study

In one paragraph

Article in Scientific reports, 2025. 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

6 authors.

Mary M BrownSchool of Integrated Health, University of New Brunswick, Saint John, NB, Canada. Maggie.Brown@unb.ca.

Stefan KuhleMedical Statistics and Informatics, Institute of Clinical Epidemiology, Public Health, Health Economics, Medical University of Innsbruck, Innsbruck, Austria.

Bruce SmithDept of Mathematics and Statistics, Dalhousie University, Halifax, NS, Canada.

Victoria M AllenDept of Obstetrics & Gynaecology, Dalhousie University, Halifax, NS, Canada.

Jennifer PayneDept of Diagnostic Radiology, Dalhousie University, Halifax, NS, Canada.

Christy G WoolcottPerinatal Epidemiology Research Unit, Depts of Obstetrics & Gynaecology and Pediatrics, Dalhousie University, Halifax, NS, Canada.

Funding

IWK Health Centre 22523New Brunswick Innovation Foundation TRF-0000000145Nova Scotia Health Research Foundation PSO-SS-2017-1358

6 · The paper itself

Abstract

Prediction of small (SGA) and large for gestational age (LGA) using routinely collected antenatal data remains suboptimal, particularly among nulliparous women. In this study, models for SGA (< 10

Indexed as

Birth WeightInfant, Small for Gestational AgeMachine LearningAdultFemaleGestational AgeHumansInfant, NewbornNova ScotiaPregnancyROC CurveBirthweightIntergenerational factorsLarge for gestational agePredictionPregnancySmall for gestational age

Identifiers

PMID41006777

DOI10.1038/s41598-025-18466-0

PMCPMC12475498

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.