Clinical characteristics and treatment outcomes in patients with <scp>double‐seronegative</scp> myasthenia gravis
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Double-seronegative myasthenia gravis (dSNMG) is defined as myasthenia gravis (MG) without detectable or low affinity antibodies to acetylcholine receptor (AChR) and muscle-specific kinase (MuSK). There are limited data on detailed clinical features and outcomes after treatment in dSNMG patients. The aim was to describe the clinical characteristics and outcomes in dSNMG patients based on MG scales. METHODS: A retrospective study was performed of patients diagnosed with MG who had negative AChR or MuSK antibodies and they were compared with an AChR-positive MG cohort. Correlations were made with data from the first and last clinic visits, between demographics, clinical characteristics, treatment and disease severity, based on the Myasthenia Gravis Foundation of America category, Myasthenia Gravis Impairment Index (MGII), Patient Acceptable Symptom State and simple single question (SSQ). RESULTS: Eighty patients met the inclusion criteria for dSNMG. The baseline MGII and SSQ scores in the dSNMG cohort showed no significant differences from the AChR group (p = 0.94 and p = 0.46). The dSNMG cohort MGII and SSQ scores improved significantly at the last clinical evaluation (p = 0.001 and p = 0.047). The MGII improvement in the AChR cohort was significantly better (p = 0.003). CONCLUSIONS: The initial severity of dSNMG based on clinical scores is similar to antibody-positive MG patients. There is significant clinical improvement in dSNMG patients after therapy, measured in the last clinical evaluation. This supports an immune pathophysiology of many dSNMG patients.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".