Evidence map›Paper›PMID 36653543›Full record

Trial reportNeurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology2023

Stroke prevention in rural residents: development of a simplified risk assessment tool with artificial intelligence.

Zhongao Ding, Liying Zhang, Miaomiao Niu, Bo Zhao, Xiaotian Liu, Wenqian Huo, Jian Hou, Zhenxing Mao, Zhenfei Wang, Chongjian Wang

Abstract readRandomized Controlled Trial
PubMed Publisher
In one paragraph

Trial report in Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 2 pooled it
–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

6 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Artificial intelligence and stroke imaging.Current opinion in neurology · 2025
    Review
  4. Article
  5. Review
  6. Review
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

10 authors.

Zhongao Ding *Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, 100 Kexue Avenue, Henan, 450001, Zhengzhou, People's Republic of China.
Liying Zhang *Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, 100 Kexue Avenue, Henan, 450001, Zhengzhou, People's Republic of China.
Miaomiao NiuDepartment of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, 100 Kexue Avenue, Henan, 450001, Zhengzhou, People's Republic of China.
Bo ZhaoDepartment of Statistics, University of Illinois at Urbana-Champaign, Champaign, IL, USA.
Xiaotian LiuDepartment of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, 100 Kexue Avenue, Henan, 450001, Zhengzhou, People's Republic of China.
Wenqian HuoDepartment of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, 100 Kexue Avenue, Henan, 450001, Zhengzhou, People's Republic of China.
Jian HouDepartment of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, 100 Kexue Avenue, Henan, 450001, Zhengzhou, People's Republic of China.
Zhenxing MaoDepartment of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, 100 Kexue Avenue, Henan, 450001, Zhengzhou, People's Republic of China.
Zhenfei WangDepartment of Software Engineering, School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou, Henan, People's Republic of China.
Chongjian WangDepartment of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, 100 Kexue Avenue, Henan, 450001, Zhengzhou, People's Republic of China. tjwcj2008@zzu.edu.cn.ORCID http://orcid.org/0000-0001-5091-6621

Funding

Discipline Key Research and Development Program of Zhengzhou University XKZDQY202002Discipline Key Research and Development Program of Zhengzhou University XKZDQY202008Foundation of National Key Program of Research and Development of China 2016YFC0900803Key Research Program of Colleges and Universities in Henan Province 21A330007National Natural Science Foundation of China 81930092National Natural Science Foundation of China 81973128Science and Technology Innovation Team Support Plan of Colleges and Universities in Henan Province 21IRTSTHN029
6 · The paper itself

Abstract

backgroundLimited studies have focused on the risk assessment of stroke in rural regions. Moreover, the application of artificial intelligence in stroke risk scoring system is still insufficient. This study aims to develop a simplified and visualized risk score with good performance and convenience for rural stroke risk assessment, which is combined with a machine learning (ML) algorithm.

methodsParticipants of the Henan Rural Cohort were enrolled in this study. The total participants (n = 38,322) were randomly split into a train set and a test set in the ratio of 7:3. An ML algorithm was used to select variables and the logistic regression was then applied to construct the scoring system. The C-statistic and the Brier score (BS) were used to evaluate the discrimination and calibration. The Framingham stroke risk profile (FSRP) and the self-reported stroke risk function (SRSRF) were chosen to be compared.

resultsThe Rural Stroke Risk Score (RSRS) was produced in this study, including age, drinking status, triglyceride, type 2 diabetes mellitus, hypertension, waist circumference, and family history of stroke. On validation, the C-statistic was 0.757 (95% CI 0.749-0.765) and the BS was 0.058 in the test set. In addition, the discrimination of RSRS was 6.02% and 7.34% higher than that of the FSRP and SRSRF, respectively.

conclusionsA well-performed scoring system for assessing stroke risk in rural residents was developed in this study. This risk score would facilitate stroke screening and the prevention of cardiovascular disease in economically underdeveloped areas.

Indexed as

Diabetes Mellitus, Type 2StrokeArtificial IntelligenceHumansRisk AssessmentRisk FactorsMachine learningRisk scoreRural regionsScreeningStroke

Identifiers

PMID36653543

What OpenQuestion holds

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Registered trials

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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.