Evidence map›Paper›PMID 40725991›Full record

ArticleJournal of Parkinson's disease2025

Early detection of Parkinson's disease: Machine learning-based prediction of UPDRS Part III scores in

Wei-Hang Guo, Xiao-Dong Yang, Zheng Ruan, Xu Wang, Dan-Zuo Zhang, Shu-Chao Song, Yi-Qiang Chen, Piu Chan

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of Parkinson's disease, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. 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

8 authors.

Wei-Hang GuoDepartment of Neurology and Neurobiology, Xuanwu Hospital Capital Medical University, Beijing, China.ORCID 0000-0003-0535-9752
Xiao-Dong YangBeijing Key Laboratory of Mobile Computing and Pervasive Devices, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China.
Zheng RuanDepartment of Neurology and Neurobiology, Xuanwu Hospital Capital Medical University, Beijing, China.
Xu WangJinan Zhongke Ubiquitous Intelligent Computing Research Institute, University of the Chinese Academy of Sciences, Beijing, China.
Dan-Zuo ZhangBeijing Key Laboratory of Mobile Computing and Pervasive Devices, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China.
Shu-Chao SongBeijing Key Laboratory of Mobile Computing and Pervasive Devices, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China.ORCID 0009-0004-0083-5881
Yi-Qiang ChenBeijing Key Laboratory of Mobile Computing and Pervasive Devices, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China.
Piu ChanDepartment of Neurology and Neurobiology, Xuanwu Hospital Capital Medical University, Beijing, China.ORCID 0000-0002-4620-1268

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BackgroundDetecting motor symptoms in Parkinson's disease (PD) at home, especially in the prodromal, is crucial for disease-modifying therapies.ObjectiveTo evaluate the effectiveness of machine learning models using smartphone-based assessments in predicting motor symptoms in untreated

Indexed as

Machine LearningMobile ApplicationsParkinson DiseaseSmartphoneAgedEarly DiagnosisFemaleHumansMaleMiddle AgedSeverity of Illness IndexTremormachine learningmotor symptomsParkinson’s diseasesmartphone assessmentuntreated de novo patientsUPDRS part III

Identifiers

PMID40725991
PMCPMC13347512

What OpenQuestion holds

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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.