Challenges managing myasthenia gravis: an international perspective
Bibliographic record
Abstract
There have been increasing breakthroughs in the diagnosis and treatment of myasthenia gravis over the past decades. However, most published research in myasthenia is conducted in developed regions, such as the US, Canada and Europe. The challenges faced in these regions may be different from other areas of the world, often with fewer resources, such as fewer neurologists, limited or no access to specialised testing for myasthenia, and limited access to some interventions. During the 14th International Conference for Myasthenia Gravis and Myasthenic Disorders, we organized a panel of neurologists and researchers who work with people living with myasthenia in different world regions. The goal was to stimulate discussion around common challenges as well as those that are specific for given areas. Ultimately, we aimed to develop networks of clinicians caring for people living with myasthenia gravis around the world, to improve patient care. We present a summary of challenges using a case format by region, and a discussion around common threads and potential next steps.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".