Congenital myasthenic syndrome: Correlation between clinical features and molecular diagnosis
Bibliographic record
Abstract
OBJECTIVES: To present phenotype features of a large cohort of congenital myasthenic syndromes (CMS) and correlate them with their molecular diagnosis. METHODS: Suspected CMS patients were divided into three groups: group A (limb, bulbar or axial weakness, with or without ocular impairment, and all the following: clinical fatigability, electrophysiology compatible with neuromuscular junction involvement and anticholinesterase agents response), group B (limb, bulbar or axial weakness, with or without ocular impairment, and at least one of additional characteristics noted in group A) and group C (pure ocular syndrome). Individual clinical findings and the clinical groups were compared between the group with a confirmed molecular diagnosis of CMS and the group without molecular diagnosis or with a non-CMS molecular diagnosis. RESULTS: Seventy-nine patients (68 families) were included in the cohort: 48 in group A, 23 in group B and 8 in group C. Fifty-one were considered confirmed CMS (30 CHRNE, 5 RAPSN, 4 COL13A1, 3 DOK7, 3 COLQ, 2 GFPT1, 1 CHAT, 1 SCN4A, 1 GMPPB, 1 CHRNA1), 7 probable CMS, 5 non-CMS and 16 unsolved. The chance of a confirmed molecular diagnosis of CMS was significantly higher for group A and lower for group C. Some individual clinical features, alterations on biopsy and electrophysiology enhanced specificity for CMS. Muscle imaging showed at least mild alterations in the majority of confirmed cases, with preferential involvement of soleus, especially in CHRNE CMS. CONCLUSIONS: Stricter clinical criteria increase the chance of confirming a CMS diagnosis, but may lose sensitivity, especially for some specific genes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".