Evidence map›Paper›PMID 42919895›Full record

ArticleNPJ digital medicine2026

Machine learning classification of digital exclusion in ageing populations across five international cohorts.

Shenwei Wan, Cong Fu, Can Lu, Zhiwen Ding, Shiyuan Wang, Lei Zhou, Xiaoguang Lei

Abstract read
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Article in NPJ digital medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

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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.

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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

7 authors.

Shenwei Wan *School of Agricultural Economics and Rural Development, Renmin University of China, Beijing, China.ORCID http://orcid.org/0009-0000-1962-6132
Cong Fu *Department of Gastrointestinal Colorectal Surgery, The First Hospital of Jilin University, Changchun, China.
Can Lu *School of Medicine and Health Management, Huazhong University of Science and Technology, Wuhan, China.ORCID http://orcid.org/0009-0009-7959-7121
Zhiwen Ding *Institute of Agricultural Information, Chinese Academy of Agricultural Sciences, Beijing, China.ORCID http://orcid.org/0009-0009-2451-793X
Shiyuan WangSchool of Agricultural Economics and Rural Development, Renmin University of China, Beijing, China.
Lei ZhouSchool of Agricultural Economics and Rural Development, Renmin University of China, Beijing, China.
Xiaoguang LeiFirst Department of Neurology, First Affiliated Hospital of Kunming Medical University, Kunming, China. leixg@ydyy.cn.

Funding

Fundamental Research Funds for the Yunnan Clinical Research Center for Neurological Diseases 202505AJ310009Reserve Talents Project of Young and Middle-aged Academic and Technical Leaders in Yunnan Province 202305AC160045Yunnan Clinical Center for Neurological and Cardiovascular Diseases YNLCYXZX2023300077Yunnan Clinical Research Center for Neurological Diseases 202505AJ310009Yunnan Revitalization Talent Support Program (2024), 535 Talent Project of First Affiliated Hospital of Kunming Medical University 2025535Q03
6 · The paper itself

Abstract

Digital exclusion may limit access to increasingly digital health and public services, yet its cross-national structure and model transportability remain uncertain. We analysed harmonised cross-sectional data from 71,689 middle-aged and older adults in CHARLS, ELSA, HRS, MHAS and SHARE. Digital exclusion was defined as no reported internet or email use, a proxy for basic digital participation. Gradient Boosting discriminated concurrent exclusion with AUCs of 0.734-0.850, but logistic regression performed similarly (0.721-0.846; differences 0.001-0.013). Within SHARE, descriptive country-level exclusion ranged from 20.1% in Denmark to 79.2% in Poland. Leave-one-cohort-out validation showed partial transportability, with mean AUC attenuation of 0.026. Cross-fitted classification scores were associated with concurrent depression (pooled adjusted OR 2.23, 95% CI 1.83-2.71). These findings support population-level characterization of digital exclusion but not prospective or individual-level risk prediction without external validation.

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