Performance of different criteria for refractory myasthenia gravis
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Defining refractory myasthenia gravis is important, as this can drive clinical decision making, for example, by escalating treatments in refractory individuals. There are several definitions of refractory myasthenia, and their performances have not been compared. Having valid and reliable criteria can help select patients in whom more aggressive treatments may be needed. METHODS: We applied five different refractory myasthenia criteria (Drachman, Mantegazza, Suh, the International Consensus Guideline (ICG), and the randomised controlled trial of eculizumab in refractory, anti-acetylcholine receptor positive, generalised myasthenia gravis (REGAIN), to a cohort of 237 patients. We compared the proportion of refractory patients among different criteria and their scores on disease severity, fatigue, and quality-of-life (QoL) scales. We also assessed the agreement for each criterion between two trained assessors. RESULTS: The Drachman, Mantegazza, and Suh criteria resulted in high proportions of refractory individuals (40.1%, 39.2%, and 38.8%, respectively), compared with the ICG and REGAIN criteria (9.7% and 3.0%, respectively). Refractory patients by the ICG and REGAIN criteria had worse disease severity, QoL, and fatigue scores, compared with patients classified as refractory by other criteria. All criteria had high agreement between raters (between 70% and 100%). CONCLUSIONS: There is high variability in the proportion of refractory myasthenia gravis patients depending on the criteria used, with ICG and REGAIN criteria capturing patients with worse disease severity. This reflects conceptual differences as to what refractory means. Further multicenter studies are needed to determine appropriate criteria for refractory myasthenia gravis.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".