ArticleMuscle & nerve2025
A Quantitative Study of Factors Influencing Myasthenia Gravis Telehealth Examination Score.
Article in Muscle & nerve, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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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.
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.
Who cites it
5 citing papers in PubMed.
- Construction of patient trajectories to model clinical trial outcomes: application to myasthenia gravis.Frontiers in digital health · 2026Article
- Bridging the Heterogeneity of Myasthenia Gravis Scores as a Foundational Step Towards the Construction of a Digital Twin.Biomedicines · 2025Article
- Machine Learning in Myasthenia Gravis: A Systematic Review of Prognostic Models and AI-Assisted Clinical Assessments.Diagnostics (Basel, Switzerland) · 2025Review
- Bridging the Heterogeneity of Myasthenia Gravis Severity Scores for Digital Twin Development.medRxiv : the preprint server for health sciences · 2025Article
- AI-Powered Telemedicine for Automatic Scoring of Neuromuscular Examinations.Bioengineering (Basel, Switzerland) · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
INTRODUCTION/
aimsThe adoption of telemedicine is generally considered as advantageous for patients and physicians, but there is limited rigorous assessment of examination strengths and limitations. We set out to perform a quantitative assessment of the limitations of a standardized examination of subjects with myasthenia gravis (MG) during video-taped telemedicine sessions.
methodsWe utilized a video bank containing recordings from 51 MG patients who completed two telemedicine-based examinations with neuromuscular experts; each recording included the MG core examination (MG-CE) and the MG activities of daily living (MG-ADL). We then applied artificial intelligence (AI) algorithms from computer vision and speech analysis to natural language processing to generate and assess the reproducibility and inter-rater reliability of the MG-CE and MG-ADL.
resultsWe successfully developed a technology to assess video examinations. While overall MG-CE scores were consistent across examiners, individual metrics showed significant variability, with up to a 25% variation in scoring within the MG-CE's range. Additionally, there was wide variability in adherence to MG-ADL instructions. These variations were attributed to differences in examiner instructions, video recording limitations, and patient disease severity. DISCUSSION: We were able to develop a system of digital analysis of neuromuscular examinations in order to assess variability in individual scoring measures of the MG-ADL and MG-CE. Our approach enabled post hoc quantitative analysis of neuromuscular examinations. Further refinement of this technology could enhance examiner training and reduce variability in clinical trial outcome measures.
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Registered trials
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.