2251 MGBase: ready, set, go! The launch of an international electronic database for patients with Myasthenia Gravis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<h3>Objectives</h3> 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. <h3>Methods</h3> 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. <h3>Results</h3> 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 presented <h3>Conclusion</h3> 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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it