Congenital myasthenic syndrome in a cohort of patients with ‘double’ seronegative myasthenia gravis
Bibliographic record
Abstract
BACKGROUND: Congenital myasthenic syndromes (CMS) have some phenotypic overlap with seronegative myasthenia gravis (SNMG). OBJECTIVE: The aim of this single center study was to assess the minimum occurrence of CMS misdiagnosed as double SNMG in a Brazilian cohort. METHODS: The genetic analysis of the most common mutations in CHRNE, RAPSN, and DOK7 genes was used as the main screening tool. RESULTS: We performed genetic analysis in 22 patients with a previous diagnosis of 'double' SNMG. In this study, one CMS patient was confirmed due to the presence of compound heterozygous variants in the CHRNE gene (c.130insG/p.Cys210Phe). CONCLUSIONS: This study confirmed that CMS due to CHNRE mutations can be mistaken for SNMG. In addition, our study estimated the prevalence of misdiagnosed CMS to be 4.5% in 'double' SNMG patients of our center. Based on our findings, genetic screening could be helpful in the diagnostic workup of patients with 'double' SNMG in whom differential diagnosis is recommended.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".