{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"medRxiv"}],"indexed":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T13:59:04Z","timestamp":1768571944264,"version":"3.49.0"},"posted":{"date-parts":[[2025,6,13]]},"group-title":"Neurology","reference-count":26,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2025,6,13]],"date-time":"2025-06-13T00:00:00Z","timestamp":1749772800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2025,6,13]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                <jats:p>Myasthenia gravis (MG) is a rare autoimmune neuromuscular disease. Clinical trials with rigorously collected data, especially for rare diseases, provide opportunities for mathematical modeling of patient outcomes over time; however, building a larger data set from multiple trials faces the challenge of harmonization of outcome measures. To accurately model MG and predict individual patient trajectories, one requires integrating three primary data types: (i) Laboratory and medication data, (ii) Electronic Health Record (EHR) data (e.g., age, sex, years since diagnosis, BMI), (iii) Disease severity scores.<\/jats:p>\n                <jats:p>Among these, MG severity scores are crucial for measuring disease progression from the patient\u2019s and clinical evaluator\u2019s perspectives. However, clinical studies often employ various scoring systems (e.g., ADL, QMG, MG-CE, MGQOL-15), making it challenging to determine the most reliable measure. In this study, we investigate the relationships among clinical outcome measures across multiple clinical studies. Our objective is to develop a robust \u201cMyasthenia Gravis Portrait\u201d that can be applied across diverse clinical studies. This standardized portrait will facilitate the creation of a virtual population of digital twins, enabling the application of machine learning techniques to a larger patient population.<\/jats:p>","DOI":"10.1101\/2025.06.13.25329566","type":"posted-content","created":{"date-parts":[[2025,6,13]],"date-time":"2025-06-13T16:40:16Z","timestamp":1749832816000},"source":"Crossref","is-referenced-by-count":0,"title":["Bridging the Heterogeneity of Myasthenia Gravis Severity Scores for Digital Twin Development"],"prefix":"10.64898","author":[{"given":"Marc","family":"Garbey","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Quentin","family":"Lesport","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8195-0141","authenticated-orcid":false,"given":"Henry J.","family":"Kaminski","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"54368","reference":[{"key":"2025061601452273000_2025.06.13.25329566v1.1","doi-asserted-by":"publisher","DOI":"10.1172\/JCI179742"},{"key":"2025061601452273000_2025.06.13.25329566v1.2","doi-asserted-by":"publisher","DOI":"10.1212\/WNL.55.1.16"},{"key":"2025061601452273000_2025.06.13.25329566v1.3","doi-asserted-by":"publisher","DOI":"10.1111\/j.1749-6632.1998.tb11015.x"},{"key":"2025061601452273000_2025.06.13.25329566v1.4","doi-asserted-by":"publisher","DOI":"10.1002\/mus.21185"},{"key":"2025061601452273000_2025.06.13.25329566v1.5","doi-asserted-by":"publisher","DOI":"10.1212\/WNL.0b013e3181dc1b1e"},{"key":"2025061601452273000_2025.06.13.25329566v1.6","doi-asserted-by":"publisher","DOI":"10.1016\/j.jns.2024.123135"},{"key":"2025061601452273000_2025.06.13.25329566v1.7","doi-asserted-by":"crossref","first-page":"630","DOI":"10.1002\/mus.27476","article-title":"Utilization of MG-ADL in myasthenia gravis clinical research and care","volume":"65","year":"2022","journal-title":"Muscle Nerve"},{"key":"2025061601452273000_2025.06.13.25329566v1.8","doi-asserted-by":"crossref","first-page":"631","DOI":"10.1016\/j.nmd.2020.06.010","article-title":"Fatigue in patients with myasthenia gravis. 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