2251 MGBase: ready, set, go! The launch of an international electronic database for patients with Myasthenia Gravis
Bibliographic record
Abstract
Objectives To develop and implement the first international observational database for patients with Myasthenia Gravis (MG) to advance collaborative outcome-based MG research and improve the quality of care for patients with MG.Methods The MGBase was developed based on the highly successful Multiple Sclerosis registry, MSBase. This approach leverages the existing IT infrastructure and governance structures of the MSBase registry. Designed to be used during regular outpatient consultations, MGBase provides a longitudinal graphical display of the patient disease course, therapies and outcomes.The development of the MGBase data entry fields and minimum data set was guided by an MG special interest group comprising national and international MG experts. Members of this group have subsequently formed the MGBase scientific leadership group responsible for determining the overall direction and scope of the MGBase registry.Results MGBase was launched in December 2021 with the first patients recruited at two Melbourne tertiary centers. It is anticipated that another four national centers and several international centers will start recruiting patients within the next 6 months. Data from the first 21 patients enrolled in MGBase demonstrates a mean age of 60.1 years (62% female) with mean disease duration of 4.74 years. Five patients had a recorded exacerbation in the last 12 months. Further clinical and demographic data will be presentedConclusion MGBase is the first observation international registry launched for patients with MG. The MGBase registry is dedicated to evaluating outcomes data in MG through collaborative international research.
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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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.167 | 0.124 |
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".