Evidence map›Paper›PMID 32444562›Full record

ReviewJournal of Parkinson's disease2020

Deep Phenotyping of Parkinson's Disease.

E Ray Dorsey, Larsson Omberg, Emma Waddell, Jamie L Adams, Roy Adams, Mohammad Rafayet Ali, Katherine Amodeo, Abigail Arky, Erika F Augustine, Karthik Dinesh and 21 more

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Journal of Parkinson's disease, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
34citing papers in PubMed, 2 pooled it
–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

34 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Structural and morphological modulation of the myocardium byBiochemistry and biophysics reports · 2026
    Article
  5. Digital Technologies for Symptom Monitoring in Parkinson Disease.Current neurology and neuroscience reports · 2026
    Review
  6. Review
  7. Article
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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

31 authors.

E Ray DorseyCenter for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA.
Larsson OmbergSage Bionetworks, Seattle, WA, USA.
Emma WaddellCenter for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA.
Jamie L AdamsCenter for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA.
Roy AdamsMachine Learning, AI and Healthcare Lab, Johns Hopkins University, Baltimore, MD, USA.
Mohammad Rafayet AliDepartment of Computer Science, University of Rochester, Rochester, NY, USA.
Katherine AmodeoDepartment of Neurology, University of Rochester Medical Center, Rochester, NY, USA.
Abigail ArkyCenter for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA.
Erika F AugustineCenter for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA.
Karthik DineshDepartment of Electrical and Computer Engineering, University of Rochester, Rochester, NY, USA.
Mohammed Ehsan HoqueDepartment of Computer Science, University of Rochester, Rochester, NY, USA.
Alistair M GliddenCenter for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA.
Stella Jensen-RobertsCenter for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA.
Zachary KabelacDepartment of Computer Science and Artificial Intelligence, Massachusetts Institute of Technology, Cambridge, MA, USA.
Dina KatabiDepartment of Computer Science and Artificial Intelligence, Massachusetts Institute of Technology, Cambridge, MA, USA.
Karl KieburtzCenter for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA.
Daniel R KinelCenter for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA.
Max A LittleSchool of Computer Science, University of Birmingham, UK.
Karlo J LizarragaCenter for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA.
Taylor MyersCenter for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA.
Sara RiggareDepartment of Women's and Children's Health, Uppsala University, Uppsala, Sweden.
Spencer Z RoseroDepartment of Medicine, University of Rochester, Rochester, NY, USA.
Suchi SariaMachine Learning, AI and Healthcare Lab, Johns Hopkins University, Baltimore, MD, USA.
Giovanni SchifittoDepartment of Neurology, University of Rochester Medical Center, Rochester, NY, USA.
Ruth B SchneiderCenter for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA.
Gaurav SharmaDepartment of Electrical and Computer Engineering, University of Rochester, Rochester, NY, USA.
Ira ShoulsonCenter for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA.
E Anna StevensonCenter for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA.
Christopher G TarolliCenter for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA.
Jiebo LuoDepartment of Computer Science, University of Rochester, Rochester, NY, USA.
Michael P McDermottCenter for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA.

Funding

The Clinical Core will support in-person and virtual research visits for three of the four Research Projects at the University of Rochester Udall CenterP50NS108676 · NINDS · UNIVERSITY OF ROCHESTER · PI DORSEY, EARL RAY · 2018 to 2022
$9.2M
NINDS NIH HHS P50 NS108676
6 · The paper itself

Abstract

Phenotype is the set of observable traits of an organism or condition. While advances in genetics, imaging, and molecular biology have improved our understanding of the underlying biology of Parkinson's disease (PD), clinical phenotyping of PD still relies primarily on history and physical examination. These subjective, episodic, categorical assessments are valuable for diagnosis and care but have left gaps in our understanding of the PD phenotype. Sensors can provide objective, continuous, real-world data about the PD clinical phenotype, increase our knowledge of its pathology, enhance evaluation of therapies, and ultimately, improve patient care. In this paper, we explore the concept of deep phenotyping-the comprehensive assessment of a condition using multiple clinical, biological, genetic, imaging, and sensor-based tools-for PD. We discuss the rationale for, outline current approaches to, identify benefits and limitations of, and consider future directions for deep clinical phenotyping.

Indexed as

PhenotypeAutonomic Nervous SystemForecastingGaitHumansParkinson DiseaseSleepAutonomic nervous systemgaitnatural historyobservational studyParkinson’s diseasephenotypereal-world datasleepsmartphonesocial behavior

Identifiers

PMID32444562
PMCPMC7458535

What Socratic holds

Textmetadata
LicenceCC BY-NC
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.