Evidence map›Paper›PMID 41172739›Full record

ArticleEBioMedicine2025

Development of self-phenotyping tools to empower patients and improve diagnostics.

Kent Shefchek, Sonja I Ziniel, Julie A McMurry, Catherine A Brownstein, John S Brownstein, Erin Rooney Riggs, Matthew Might, Damian Smedley, Amy Clugston, Alan H Beggs and 5 more

Abstract read
In one paragraph

Article in EBioMedicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–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

1 citing paper in PubMed.

  1. Disparate language and model effects on AI-based translation and recognition of genetic conditions.Journal of the American Medical Informatics Association : JAMIA · 2026
    Article
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

15 authors.

Kent ShefchekOregon State University, Corvallis, OR, USA.
Sonja I ZinielSection of Pediatric Hospital Medicine, Department of Pediatrics, Children's Hospital Colorado and School of Medicine, University of Colorado, Aurora, CO, USA.
Julie A McMurryDepartment of Genetics, University of North Carolina at Chapel Hill, 120 Mason Farm Rd, Chapel Hill, NC, 27599, USA.
Catherine A BrownsteinDivision of Genetics and Genomics, Department of Pediatrics, The Manton Center for Orphan Disease Research, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA.
John S BrownsteinComputational Epidemiology Laboratory, Department of Pediatrics, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA.
Erin Rooney RiggsAutism and Developmental Medicine Institute, Geisinger, Danville, PA, USA.
Matthew MightHugh Kaul Precision Medicine Institute, The University of Alabama at Birmingham, AL, USA.
Damian SmedleyWilliam Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK.
Amy ClugstonDYRK1A Syndrome US, USA.
Alan H BeggsDivision of Genetics and Genomics, Department of Pediatrics, The Manton Center for Orphan Disease Research, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA.
Heather PatersonDivision of Genetics and Genomics, Department of Pediatrics, The Manton Center for Orphan Disease Research, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA.
Peter N RobinsonMedical Computer Science and Artificial Intelligence, Berlin Institute of Health at Charité (BIH), Rahel Hirsch Center for Translational Medicine, Luisenstraße 65, 10117, Berlin, Germany.
Nicole A VasilevskyCritical Path Institute, 1840 E River Rd #100, Tucson, AZ, 85718, USA.
Ingrid A HolmDivision of Genetics and Genomics, Department of Pediatrics, The Manton Center for Orphan Disease Research, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA. Electronic address: ingrid.holm@childrens.harvard.edu.
Melissa A HaendelDepartment of Genetics, University of North Carolina at Chapel Hill, 120 Mason Farm Rd, Chapel Hill, NC, 27599, USA. Electronic address: melissa@tislab.org.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDeep phenotyping is important for rare disease diagnostics, often using the Human Phenotype Ontology (HPO). Patients are an under-utilised source; to facilitate self-phenotyping we previously "translated" HPO into plain language. However, self-reported data has not been assessed to date for diagnostic efficacy nor patient opportunities for collaboration with clinical diagnosticians.

methodsApproximately one-third of the HPO is translatable to layperson HPO terms. We used two instruments to evaluate patients' use of the layperson HPO - the GenomeConnect survey mapped to layperson HPO terms, and a natively layperson HPO-based application, Phenotypr. We created phenotype profiles for 7344 Mendelian diseases for each instrument, representing the theoretical maximum performance that patients might achieve. To explore real-world capabilities, we randomised participants with diagnosed genetic diseases to use GenomeConnect, Phenotypr, or both instruments. For each diagnosed disease, we compared the layperson HPO profile to the patient-completed profile for each instrument. We also performed qualitative interviews.

findingsBoth instruments performed well in retrieving the correct disease (area under the curve 0.991 and 0.954). Profiles resulting from participant responses to the GenomeConnect survey were more accurate than Phenotypr. Phenotypr had a tighter distribution of scores for respondents who did both instruments and was more precise. We conducted interviews and generally participants preferred the GenomeConnect multiple choice format over the autocomplete Phenotypr format and valued the opportunity to contribute to their diagnostic workup.

interpretationOur results demonstrate that individuals are capable of providing rich phenotype data and suggest that self-phenotyping could be used to supplement profiles created by clinicians, providing a more robust foundation for rare disease patient engagement and reducing burden on clinical diagnosticians. Further user interface innovation is needed to garner maximally useful phenotype data from patients.

fundingThis research was supported by PCORI #HSRP20181624. The Manton Foundation provided philanthropic support for patient studies. NIH P50HD105351 provided support for genotyping patients at BCH.

Indexed as

Genetic Diseases, InbornPhenotypeRare DiseasesFemaleHumansMaleSurveys and QuestionnairesClinical geneticsDiagnosticsOntologyPhenotypingRare disease

Identifiers

PMID41172739
PMCPMC12616013

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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.