<scp>MGBase</scp>: A Global, Observational Registry for Collaborative Research in Myasthenia Gravis
Bibliographic record
Abstract
INTRODUCTION/AIMS: Patient registries are valuable tools for outcomes research in rare diseases such as myasthenia gravis (MG). Existing MG registries are limited by factors including a lack of geographical scope. MGBase has been designed as a global, observational registry aimed at studying clinical practice outcomes in MG. METHODS: MGBase was developed with the support of the independent MSBase Foundation. An international scientific leadership group (SLG) established a minimum dataset and outcome measures. Data are entered on a purpose-designed platform in real time and held in a web-based registry. Members can request access to the global dataset for investigator-driven substudies. RESULTS: MGBase data collection commenced in October 2021. From inception until April 2024, 565 patients from 16 clinics and 8 countries were enrolled. The cohort is 56% female, with a mean age of 57 (SD19) years at the last visit and a median disease duration of 5 (IQR 1.8, 10.8) years. Seventy-six percent of patients are acetylcholine receptor antibody positive (AChR ab+) and 7% have antibodies to muscle-specific kinase (MuSK ab+). At diagnosis, 33% of patients had ocular MG. Immunotherapy was used in 87% of patients. A minority of patients (7%) required three or more concurrent immunotherapies. Thymectomy was performed in 24% of patients. DISCUSSION: MGBase is a global registry for collaborative research in MG. Interim analysis of registry data shows disease characteristics similar to those previously published. As global enrollments increase, the registry will generate clinical practice evidence of treatment outcomes, safety, and disease prognostic markers.
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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 | 0.011 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.005 |
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".