Patient-reported impact of myasthenia gravis in the real world: protocol for a digital observational study (MyRealWorld MG)
Bibliographic record
Abstract
INTRODUCTION: Myasthenia gravis (MG) is a rare, chronic, autoimmune disease, mediated by immunoglobulin G antibodies, which causes debilitating muscle weakness. As with most rare diseases, there is little patient-reported data with which to understand and address patient needs. This study explores the impact of MG in the real world from the patient perspective. METHODS AND ANALYSIS: This is a 2-year prospective, observational, digital, longitudinal study of adults with MG, resident in the following countries: the USA, Japan, Germany, France, the UK, Italy, Spain, Canada and Belgium. The planned sample size is 2000. Recruitment will be community based, via patient advocacy groups, social media and word of mouth. Participants will use a smartphone application (app) to check eligibility, provide consent and contribute data. Planned data entry is as follows: (1) personal profile on enrollment-covering demographics, MG characteristics and previous care; (2) monthly event tracker-current treatments, healthcare visits, treatment-related adverse events, productivity losses; (3) monthly selection of validated generic and disease-specific patient-reported outcomes instruments: EQ-5D-5L, Myasthenia Gravis Activities of Daily Living, Myasthenia Gravis Quality of Life 15-item revised scale, Hospital Anxiety and Depression Scale and Health Utilities Index III. Analyses are planned for when the study has been running in most countries for approximately 6, 12, 18 and 24 months. ETHICS AND DISSEMINATION: The study protocol has been reviewed and granted ethics approval by Salus IRB for participants resident in the following countries: Germany, the UK and the US. Local ethics approval is being sought for the following study countries: Belgium, Canada, France, Italy, Japan and Spain. Study results will be communicated to the public and participants via conference presentations and journal publications, as well as regular email, social media and in-application communication. TRIAL REGISTRATION NUMBER: NCT04176211.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.026 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.040 | 0.007 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".