Evidence mapPaperPMID 35886437Full record

ArticleInternational journal of environmental research and public health2022

Risk Assessment for Birth Defects in Offspring of Chinese Pregnant Women.

Pengfei Qu, Doudou Zhao, Mingxin Yan, Danmeng Liu, Leilei Pei, Lingxia Zeng, Hong Yan, Shaonong Dang

Open access · goldAbstract read
In one paragraph

Article in International journal of environmental research and public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
1.5field-weighted citation impact, top 17% 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

7 citing papers in PubMed, 9 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

8 authors at 2 institutions in 1 country.

Pengfei QuThe NCH Key Laboratory of Neonatal Diseases, National Children's Medical Center, Children's Hospital of Fudan University, Shanghai 201102, China.
Doudou ZhaoTranslational Medicine Center, Northwest Women's and Children's Hospital, No. 1616 Yanxiang Road, Xi'an 710061, China.
Mingxin YanDepartment of Epidemiology and Health Statistics, School of Public Health, Xi'an Jiaotong University Health Science Center, No. 76 Yanta West Road, Xi'an 710061, China.
Danmeng LiuTranslational Medicine Center, Northwest Women's and Children's Hospital, No. 1616 Yanxiang Road, Xi'an 710061, China.
Leilei PeiDepartment of Epidemiology and Health Statistics, School of Public Health, Xi'an Jiaotong University Health Science Center, No. 76 Yanta West Road, Xi'an 710061, China.
Lingxia ZengDepartment of Epidemiology and Health Statistics, School of Public Health, Xi'an Jiaotong University Health Science Center, No. 76 Yanta West Road, Xi'an 710061, China.
Hong YanDepartment of Epidemiology and Health Statistics, School of Public Health, Xi'an Jiaotong University Health Science Center, No. 76 Yanta West Road, Xi'an 710061, China.
Shaonong DangDepartment of Epidemiology and Health Statistics, School of Public Health, Xi'an Jiaotong University Health Science Center, No. 76 Yanta West Road, Xi'an 710061, China.ORCID 0000-0002-6980-8169
Xi'an Jiaotong University · CNNorthwest Women's and Children's Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveThis study aimed to develop a nomogram for the risk assessment of any type of birth defect in offspring using a large birth-defect database in Northwest China.

methodsThis study was based on a birth-defect survey, which included 29,204 eligible women who were pregnant between 2010 and 2013 in the Shaanxi province of Northwest China. The participants from central Shaanxi province were assigned to the training group, while the subjects from the south and north of Shaanxi province were assigned to the external validation group. The primary outcome was the occurrence of any type of birth defect in the offspring. A multivariate logistic regression model was used to establish a prediction nomogram, while the discrimination and calibration were evaluated by external validation.

resultsThe multivariate analyses revealed that household registration, history of miscarriages, family history of birth defects, infection, taking medicine, pesticide exposure, folic acid supplementation, and single/twin pregnancy were significant factors in the occurrence of birth defects. The area under the receiver operating characteristic curve (AUC) in the prediction model was 0.682 (95% CI 0.653 to 0.710) in the training set. The validation set showed moderate discrimination, with an AUC of 0.651 (95% CI 0.614 to 0.689). Additionally, the prediction model had a good calibration (HL χ

conclusionsWe developed a nomogram risk model for any type of birth defect in a Chinese population based on important modifying factors in pregnant women. This risk-prediction model could be a tool for clinicians to assess the risk of birth defects and promote health education.

Indexed as

Health PromotionPregnant PeopleChinaFemaleHumansLogistic ModelsPregnancyRisk AssessmentRisk Factorsbirth defectsChinese populationnomogramprediction modelpregnant women

Identifiers

PMID35886437
PMCPMC9319985
OpenAlexW4286240450

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.