Evidence map›Paper›PMID 41465739›Full record

ArticleLife (Basel, Switzerland)2025

Neonatal and Birth Risk Factors for Type 1 Diabetes Mellitus: Prediction Using an Artificial Neural Network.

Claudiu Cobuz, Mădălina Ungureanu-Iuga, Maricela Cobuz

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Life (Basel, Switzerland), 2025. 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.

  1. 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

3 authors.

Claudiu CobuzFaculty of Medicine and Biological Sciences, Ştefan cel Mare University of Suceava, 13th Universităţii Street, 720229 Suceava, Romania.ORCID 0009-0005-2795-8491
Mădălina Ungureanu-IugaIntegrated Center for Research, Development and Innovation in Advanced Materials, Nanotechnologies, and Distributed Systems for Fabrication and Control (MANSiD), Ştefan cel Mare University of Suceava, 13th Universităţii Street, 720229 Suceava, Romania.ORCID 0000-0003-1314-5957
Maricela CobuzFaculty of Medicine and Biological Sciences, Ştefan cel Mare University of Suceava, 13th Universităţii Street, 720229 Suceava, Romania.ORCID 0009-0003-8343-5025

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Type 1 Diabetes Mellitus (T1D) can be related to various factors, including neonatal and perinatal conditions. This study investigated the impact of neonatal and perinatal factors-Apgar score, birth weight, feeding type, sex, and delivery type-on the risk of Type 1 Diabetes Mellitus and evaluated predictive models. A cohort of 327 patients was analyzed using correlations, General Linear Model, and artificial neural network. T1D patients showed higher birth weight, lower Apgar score, a predominance of formula feeding, and more cesarean deliveries. Diabetes risk showed a moderate positive correlation with birth weight class and nutrition type (

Indexed as

Apgar scorebirth weightDiabetes Mellitusinfant nutritionmachine learningmultifactorial analysis

Identifiers

PMID41465739
PMCPMC12733860

What Socratic holds

Textmetadata
LicenceCC BY
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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.