Evidence map›Paper›PMID 30877513›Full record

ArticleMedical & biological engineering & computing2019

Predicting Down syndrome and neural tube defects using basic risk factors.

Momina T Khattak, Eko Supriyanto, Muhammad N Aman, Rania H Al-Ashwal

Abstract read
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In one paragraph

Article in Medical & biological engineering & computing, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.8field-weighted citation impact, top 14% of its field
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

3 citing papers in PubMed, 5 citations in OpenAlex.

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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

4 authors at 2 institutions in 2 countries.

Momina T KhattakSchool of Biomedical Engineering and Health Sciences, Universiti Teknologi Malaysia, Johor, Malaysia. momina.naveed1@gmail.com.ORCID http://orcid.org/0000-0002-9642-1082
Eko SupriyantoSchool of Biomedical Engineering and Health Sciences, Universiti Teknologi Malaysia, Johor, Malaysia.
Muhammad N AmanSchool of Computing, National University of Singapore, Singapore, Singapore.
Rania H Al-AshwalSchool of Biomedical Engineering and Health Sciences, Universiti Teknologi Malaysia, Johor, Malaysia.
University of Technology Malaysia · MYNational University of Singapore · SG

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Congenital anomalies are not only one of the main killers for infants but also one of the major causes of deaths under 5. Among congenital anomalies, Down syndrome or trisomy 21 (T-21) and neural tube defects (NTDs) are considered the most common. Expectant mothers in developing countries may not have access to or may not afford the advanced prenatal screening tests. To solve this issue, this paper explores the practicality of using only the basic risk factors for developing prediction models as a tool for initial risk assessment. The prediction models are based on logistic regression. The results show that the prediction models do not have a high balanced classification rate. However, these models can still be used as an effective tool for initial risk assessment for T-21 and NTDs by eliminating at least 50% of the cases with no or low risk. Graphical Abstract Prenatal Risk Assessment of Trisomy-21 and Neural Tube Defects.

Indexed as

Models, TheoreticalDown SyndromeFemaleHumansLogistic ModelsMaleMaternal AgeNeural Tube DefectsPregnancyPrenatal DiagnosisRisk FactorsROC CurveLogistic regressionNeural tube defectsPrediction modelPrenatal screeningTrisomy 21

Identifiers

PMID30877513
OpenAlexW2921232023

What Socratic holds

Textmetadata
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.