Evidence map›Paper›PMID 38196387›Full record

ArticleInformatics for health & social care2024

Algorithmic identification of persons with dementia for research recruitment: ethical considerations.

Alex John London, Jason Karlawish, Emily A Largent, Spencer Phillips Hey, Ellen P McCarthy

Abstract read
In one paragraph

Article in Informatics for health & social care, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Paving the way for Alzheimer's disease blood-based biomarkers in primary care.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025
    Review
  3. 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

5 authors.

Alex John LondonCenter for Ethics and Policy, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA.
Jason KarlawishDepartment of Medical Ethics and Health Policy, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA.
Emily A LargentDepartment of Medical Ethics and Health Policy, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA.
Spencer Phillips HeyPrism Analytic Technologies, Cambridge, Massachusetts, USA.
Ellen P McCarthyHinda and Arthur Marcus Institute for Aging Research, Hebrew SeniorLife, Boston, Massachusetts, USA.

Funding

Training Core (I)U54AG063546 · NIA · BROWN UNIVERSITY · PI JOSEPH E. GAUGLER · 2019 to 2026
$125.9M
Research Education ComponentP30AG072979 · NIA · UNIVERSITY OF PENNSYLVANIA · PI DAVID A WOLK · 2021 to 2026
$24.8M
NIA NIH HHS P30 AG072979NIA NIH HHS U54 AG063546
6 · The paper itself

Abstract

Underdiagnosis, misdiagnosis, and patterns of social inequality that translate into unequal access to health systems all pose barriers to identifying and recruiting diverse and representative populations into research on Alzheimer's disease and Alzheimer's disease related dementias. In response, some have turned to algorithms to identify patients living with dementia using information that is associated with this condition but that is not as specific as a diagnosis. This paper explains six ethical issues associated with the use of such algorithms including the generation of new, sensitive, identifiable medical information for research purposes without participant consent, issues of justice and equity, risk, and ethical communication. It concludes with a discussion of strategies for addressing these issues and prompting valuable research.

Indexed as

Alzheimer DiseaseHumansalgorithmic identificationArtificial intelligencedementiaethicsresearch ethics

Identifiers

PMID38196387
PMCPMC11001531

What OpenQuestion holds

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LicenceTDM
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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 OpenQuestion graph.