Evidence map›Paper›PMID 40297353›Full record

ArticleDigital health

Outcome measurement instruments in neurodegenerative diseases: Comprehensive analysis and platform construction.

Hui Zong, Jiaxue Cha, Liang Tao, Muyun Shi, Yan Zhao, Ru Zhang, Bairong Shen

Abstract read
In one paragraph

Article in Digital health. 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

7 authors.

Hui ZongDepartment of Neurology and Institutes for Systems Genetics, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, China.ORCID https://orcid.org/0000-0002-9142-5017
Jiaxue ChaInstitute for Regenerative Medicine, Shanghai East Hospital, Shanghai Key Laboratory of Signaling and Disease Research, School of Life Sciences and Technology, Tongji University, Shanghai, China.ORCID https://orcid.org/0000-0002-5917-5441
Liang TaoFaculty of Business Information, Shanghai Business School, Shanghai, China.ORCID https://orcid.org/0000-0001-6233-9518
Muyun ShiDepartment of Neurology, Qingyang People's Hospital, Qingyang, China.ORCID https://orcid.org/0009-0003-4216-9058
Yan ZhaoDepartment of Prevention Healthcare, Southwest Hospital, First Affiliated Hospital of the Army Medical University, Chongqing, China.ORCID https://orcid.org/0000-0002-4763-6634
Ru ZhangInstitute for Regenerative Medicine, Shanghai East Hospital, Shanghai Key Laboratory of Signaling and Disease Research, School of Life Sciences and Technology, Tongji University, Shanghai, China.ORCID https://orcid.org/0009-0009-3569-2848
Bairong ShenDepartment of Neurology and Institutes for Systems Genetics, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, China.ORCID https://orcid.org/0000-0003-2899-1531

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: The selection of appropriate outcome measurement instruments (OMIs) in neurodegenerative disease (NDD) researches remains complex and often inconsistent. This study aims to consolidate knowledge on OMIs applied in NDD over the last two decades and to develop outcome measurement instruments in neurodegenerative diseases (NDDOMI), a web-based knowledge platform for OMIs selection. Methods: We collected clinical trials from the past two decades across six prevalent NDDs from ClinicalTrials.gov. OMIs were manually extracted, categorized, and normalized by experts. We analyzed OMI utilization patterns, cross-disease applicability, and temporal trends. Additionally, we developed NDDOMI, a knowledge resource platform, using Django, ECharts, and JavaScript. The usability of NDDOMI was assessed using the System Usability Scale and Net Promoter Score. Results: The NDDOMI platform provides an interface covering 4619 clinical trials and 2494 OMIs. On average, 4.4 instruments were utilized per trial. Fifteen instruments are shared across six NDDs. 76.75% of the trials employed more than one instruments, often in combination. A positive correlation was observed between the number of trials and the diversity of OMIs employed. The top 10 frequently employed instruments for each disease were identified, and their utilization has increased over time. The usability assessment demonstrated excellent user satisfaction with an average SUS score of 88.19. Conclusions: This study introduces a comprehensive analysis of OMI usage trends in NDD research and introduces NDDOMI platform for OMI navigation and selection. NDDOMI serves as a valuable resource for optimizing clinical research design and standardizing outcome measurement in NDD management. The NDDOMI platform is available at http://bioinf.org.cn:3134/index/.

Indexed as

clinical decision supportclinical trial optimizationDigital health platformsneurodegenerative disease managementoutcome measurement instruments

Identifiers

PMID40297353
PMCPMC12035495

What OpenQuestion holds

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Registered trials

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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.