Evidence map›Paper›PMID 36960420›Full record

ArticleIEEE journal of biomedical and health informatics2023

A Novel Analysis of Compound Muscle Action Potential Scan: Staircase Function Fitting and StairFit Motor Unit Number Estimation.

Maoqi Chen, Zhiyuan Lu, Ya Zong, Xiaoyan Li, Ping Zhou

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Article in IEEE journal of biomedical and health informatics, 2023. 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
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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

5 authors.

Maoqi ChenSchool of Rehabilitation Science and Engineering, University of Health and Rehabilitation Sciences, Qingdao, Shandong 266072, China.
Zhiyuan LuSchool of Rehabilitation Science and Engineering, University of Health and Rehabilitation Sciences, Qingdao, Shandong 266072, China.
Ya ZongDepartment of Rehabilitation Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.
Xiaoyan LiDepartment of Neurology, Medical College of Wisconsin, Milwaukee, WI 53226 USA; Fischell Department of Bioengineering, University of Maryland at College Park, College Park, MD 20742 USA.ORCID 0000-0002-1144-0648
Ping ZhouSchool of Rehabilitation Science and Engineering, University of Health and Rehabilitation Sciences, Qingdao, Shandong 266072, China.ORCID 0000-0002-4394-2677

Funding

High-Density surface EMG based CMAP scan for motor unit number estimationR21NS113716 · NINDS · MEDICAL COLLEGE OF WISCONSIN · PI BARKHAUS, PAUL E, LI, XIAOYAN · 2020 to 2020
$432k
NINDS NIH HHS R21 NS113716
6 · The paper itself

Abstract

Compound muscle action potential (CMAP) scan provides a detailed stimulus-response curve for examination of neuromuscular disease. The objective of the study is to develop a novel CMAP scan analysis to extract motor unit number estimation (MUNE) and other physiological or diagnostic information. A staircase function was used as the basic mathematical model of the CMAP scan. An optimal staircase function fitting model was estimated for each given number of motor units, and the fitting model with the minimum number of motor units that meets a predefined error requirement was accepted. This yields MUNE as well as the spike amplitude and activation threshold of each motor unit that contributes to the CMAP scan. The significance of the staircase function fit was confirmed using simulated CMAP scans with different motor unit number (20, 50, 100 and 150) and baseline noise (1

Indexed as

Motor NeuronsMuscle, SkeletalAction PotentialsElectromyographyHumansMuscle Fibers, SkeletalReproducibility of ResultsCMAP scanCompound muscle action potential (CMAP)motor unit number estimation (MUNE)spinal cord injury (SCI)staircase functionStairFit MUNE

Identifiers

PMID36960420
PMCPMC10032645

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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.