Drug‐refractory myasthenia gravis: Clinical characteristics, treatments, and outcome
Bibliographic record
Abstract
OBJECTIVE: To describe the clinical characteristics and outcomes in patients with refractory myasthenia gravis (MG) and to determine the effectiveness and side effects of the drugs used for their treatment. METHODS: This observational retrospective cross-sectional multicenter study was based on data from the Spanish MG Registry (NMD-ES). Patients were considered refractory when their MG Foundation of America post-interventional status (MGFA-PIS) was unchanged or worse after corticosteroids and two or more other immunosuppressive agents. Clinical and immunologic characteristics of drug-refractory patients, efficiency and toxicity of drugs used, and outcome (MGFA-PIS) at end of follow-up were studied. RESULTS: We included 990 patients from 15 hospitals. Eighty-four patients (68 of 842 anti-acetylcholine receptor [AChR], 5 of 26 anti-muscle-specific tyrosine kinase [MusK], 10 of 120 seronegative, and 1 of 2 double-seropositive patients) were drug refractory. Drug-refractory patients were more frequently women (p < 0.0001), younger at onset (p < 0.0001), and anti-MuSK positive (p = 0.037). Moreover, they more frequently presented a generalized form of the disease, bulbar symptoms, and life-threatening events (p < 0.0001; p = 0.018; and p = 0.002, respectively) than non-drug-refractory patients. Mean follow-up was 9.8 years (SD 4.5). Twenty-four (50%) refractory patients had side effects to one or more of the drugs. At the end of follow-up, 42.9% of drug-refractory patients (42.6% of anti-AChR, 100% of anti-MuSK, and 10% of seronegative patients) and 79.8% of non-drug-refractory patients (p < 0.0001) achieved remission or had minimal manifestations. Eighty percent of drug-refractory-seronegative patients did not respond to any drug tested. INTERPRETATION: In this study, 8.5% of MG patients were drug-refractory. New more specific drugs are needed to treat drug-refractory MG patients.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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 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".