Evidence map›Paper›PMID 40502449›Full record

ReviewMayo Clinic proceedings. Digital health2025

Much More Than the Malady: The Promise of a Web-Based Digital Platform Incorporating Self-Report for Research and Clinical Care in Mild Cognitive Impairment.

Andrew McGarry, Oliver Roesler, Jackson Liscombe, Michael Neumann, Hardik Kothare, Abhishek Hosamath, Lakshmi Arbatti, Anusha Badathala, Stephen Ruhmel, Bryan J Hansen and 6 more

Abstract readReview
In one paragraph

Review in Mayo Clinic proceedings. Digital health, 2025. 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. Review
  2. Article
  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

16 authors.

Andrew McGarryDepartment of Neurology, Cooper University Healthcare at Rowan University, Camden, NJ.
Oliver RoeslerModality.AI, Inc, San Francisco, CA.
Jackson LiscombeModality.AI, Inc, San Francisco, CA.
Michael NeumannModality.AI, Inc, San Francisco, CA.
Hardik KothareModality.AI, Inc, San Francisco, CA.
Abhishek HosamathModality.AI, Inc, San Francisco, CA.
Lakshmi ArbattiModality.AI, Inc, San Francisco, CA.
Anusha BadathalaSan Francisco Veterans Affairs Health Care System, San Francisco, CA.
Stephen RuhmelJanssen Research & Development, LLC, a Johnson & Johnson Company, New Brunswick, NJ.
Bryan J HansenJanssen Research & Development, LLC, a Johnson & Johnson Company, New Brunswick, NJ.
Madeline QuallJanssen Research & Development, LLC, a Johnson & Johnson Company, New Brunswick, NJ.
Sandrine IstasICON Strategic Solutions, ICON plc., Belgium.
Arthur WallaceSan Francisco Veterans Affairs Health Care System, San Francisco, CA.
David Suendermann-OeftModality.AI, Inc, San Francisco, CA.
Vikram RamanarayananModality.AI, Inc, San Francisco, CA.
Ira ShoulsonModality.AI, Inc, San Francisco, CA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Traditional clinical trials in neurodegenerative disorders have utilized combinations of examination-based outcomes, global assessments by investigators and participants, and scales aimed at function, some of which are patient-reported outcomes. It is debatable whether these tools optimally convey therapeutic efficacy. A complementary approach using digital biomarkers to surpass exam-based limitations for detecting physical change coupled with a direct report from participants on what their sources of suffering are could be a useful advance in reporting beneficial effects of interventions, particularly if changes track together. We sought to determine the feasibility of remotely assessing speech, facial features, and cognition in an mild cognitive impairment (MCI) population, whether those extracted features could distinguish MCI from controls, and to explore what self-reported problems could reveal about the MCI experience. Our web-based platform was easy to use and revealed facial features in particular as capable of discriminating MCI from controls. Using the features that showed a statistically significant difference between cohorts (

Identifiers

PMID40502449
PMCPMC12152880

What OpenQuestion holds

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LicenceCC BY
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 OpenQuestion graph.