Evidence map›Paper›PMID 42180300›Full record

ArticleFrontiers in artificial intelligence2026

Attrition and representativeness in development and validation of online symptom checkers-a case study on the

Floor Dijkstra Zegers, Ling Qin, Daniyal Selani, Georgy Gomon, Tjardo Maarseveen, Kasper Glas, Astrid van Tubergen, Yvonne Goekoop Ruiterman, Marcel Reinders, Erik van den Akker and 6 more

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Article in Frontiers in artificial intelligence, 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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1 · What the graph read from it

What it found

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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.

No citing paper in PubMed yet.

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

16 authors.

Floor Dijkstra ZegersDepartment of Biomedical Data Sciences, Leiden University Medical Center, Leiden, Netherlands.
Ling QinDepartment of Rheumatology, Leiden University Medical Center, Leiden, Netherlands.
Daniyal SelaniDepartment of Intelligent Systems, Pattern Recognition and Bioinformatics, Delft University of Technology, Delft, Netherlands.
Georgy GomonDepartment of Biomedical Data Sciences, Leiden University Medical Center, Leiden, Netherlands.
Tjardo MaarseveenDepartment of Rheumatology, Leiden University Medical Center, Leiden, Netherlands.
Kasper GlasReumazorg Zuid-West Nederland, Roosendaal, Netherlands.
Astrid van TubergenDepartment of Rheumatology, Maastricht University Medical Centre+, Maastricht, Netherlands.
Yvonne Goekoop RuitermanDepartment of Clinical Epidemiology, Haga Hospital, The Hague, Netherlands.
Marcel ReindersDepartment of Intelligent Systems, Pattern Recognition and Bioinformatics, Delft University of Technology, Delft, Netherlands.
Erik van den AkkerDepartment of Intelligent Systems, Pattern Recognition and Bioinformatics, Delft University of Technology, Delft, Netherlands.
Corne Baatenburg de JongResearch, Innovation and Impact Funding, ReumaNederland (Dutch Arthritis Society), Amsterdam, Netherlands.
Lars KlareskogDivision of Rheumatology, Department of Medicine, Karolinska Institutet, Stockholm, Sweden.
Barbara AxnäsElsa Science, Stockholm, Sweden.
Reinhard BosDepartment of Rheumatology, Medical Center Leeuwarden, Leeuwarden, Netherlands.
Saskia le CessieDepartment of Biomedical Data Sciences, Leiden University Medical Center, Leiden, Netherlands.
Rachel KnevelDepartment of Rheumatology, Leiden University Medical Center, Leiden, Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Online symptom checkers are often developed and validated on data subject to self-selection and selective attrition, potentially introducing biases in prediction models. Objectives: To assess recruitment, selection, and attrition patterns in a large Dutch online symptom checker for musculoskeletal complaints and to evaluate potential biases by comparing participant characteristics across recruitment sources and with external target populations. Methods: Using data from the online Dutch Results: The total study group of answers to the questionnaire comprised 31,457 responders, of which 50% ( Conclusion: Patterns of recruitment and attrition produced differences in participant characteristics. Healthcare-based recruitment yielded participants resembling intended target populations, and follow-up survey responders differed on some points from nonresponders. Awareness of these selection processes is essential when using real-world symptom checker data for model development.

Indexed as

digital decision support systemgeneralizabilityhealth informaticsmusculoskeletal complaintsprediction modelreal-world data

Identifiers

PMID42180300
PMCPMC13194141

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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.