Evidence map›Paper›PMID 41737895›Full record

ArticleMethodsX2026

A comprehensive electrooculography measurement system and protocols for screening myasthenia gravis.

Tien Loc Le, Mark I Boulos, Donna Yang, Sarah Berger, Mona Irannejad, César Morales Figueroa, Kevin E Thorpe, Brian Murray, Sridhar Krishnan, Arun N E Sundaram and 2 more

Abstract read
In one paragraph

Article in MethodsX, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

12 authors.

Tien Loc LeToronto Metropolitan University, Toronto, Ontario, Canada.
Mark I BoulosHurvitz Brain Sciences Research Program, Sunnybrook Research Institute, Department of Medicine, Division of Neurology, University of Toronto, Sleep Laboratory, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada.
Donna YangHurvitz Brain Sciences Research Program, Sunnybrook Research Institute, Department of Medicine, Division of Neurology, University of Toronto, Sleep Laboratory, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada.
Sarah BergerHurvitz Brain Sciences Research Program, Sunnybrook Research Institute, Department of Medicine, Division of Neurology, University of Toronto, Sleep Laboratory, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada.
Mona IrannejadUniversity Health Network - Toronto General Hospital, Toronto, Ontario, Canada.
César Morales FigueroaUniversity Health Network - Toronto General Hospital, Toronto, Ontario, Canada.
Kevin E ThorpeDalla Lana School of Public Health - University of Toronto, Toronto, Ontario, Canada.
Brian MurrayHurvitz Brain Sciences Research Program, Sunnybrook Research Institute, Department of Medicine, Division of Neurology, University of Toronto, Sleep Laboratory, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada.
Sridhar KrishnanToronto Metropolitan University, Toronto, Ontario, Canada.
Arun N E SundaramHurvitz Brain Sciences Research Program, Sunnybrook Research Institute, Department of Medicine, Division of Neurology, University of Toronto, Sleep Laboratory, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada.
Hans KatzbergUniversity Health Network - Toronto General Hospital, Toronto, Ontario, Canada.
Karthikeyan UmapathyToronto Metropolitan University, Toronto, Ontario, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Myasthenia gravis (MG) is a neuromuscular disorder that can precipitate serious and fatal complications, especially when respiratory muscles are affected. Current electrodiagnostic methods, such as Single Fiber Electromyography (SFEMG) and Repetitive Nerve Stimulation (RNS), have notable limitations. While SFEMG is sensitive to neuromuscular abnormalities rather than being specific for MG, it is also a semi-invasive procedure that requires supervision by specialist clinicians and an expensive clinical setup. Conversely, RNS is highly specific for MG, but its sensitivity decreases in cases of low severity. Prior studies, including our previous work, have demonstrated that the manifestation of MG in affecting eye movements can be indirectly quantified using electrooculography (EOG) signals. This non-invasive method could develop into a widely used and easily deployed screening method, especially for early detection of MG using readily obtainable biomarkers. With this goal in mind, our research team developed data collection protocols using a standardized eye movement protocol that could be implemented in a standard clinical setting to acquire EOG data. In this article, we present the experimental setup, test protocols, sample outcomes, and preliminary analysis approaches. Common artifacts encountered, as well as technical and logistical challenges associated with such a setup, are also discussed.1.An approach to non-invasively detect and quantify MG using eye movement signals.2.Data collection protocols for acquiring eye movement signals designed for MG quantification3.Novel eye movement-based biomarkers in quantifying MG.

Indexed as

ElectrooculogramElectrooculogram protocolsMyasthenia gravisSignal acquisitionSignal processing

Identifiers

PMID41737895
PMCPMC12927095

What OpenQuestion holds

Textmetadata
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.