Familial Autoimmune Myasthenia Gravis: Four Patients Involving Three Generations
Bibliographic record
Abstract
BACKGROUND: Familial autoimmune myasthenia gravis (MG) is rare, although a genetic role for the development of autoimmune MG is suggested by concordance in monozygotic twins and the increased frequency of other autoimmune diseases in family members of myasthenics. METHODS: A patient with a family history of MG was evaluated in hospital. Relatives were interviewed and medical records examined for details regarding the diagnosis of MG in three other family members. RESULTS: The index case first experienced symptoms of MG at age 75 years. She developed generalized MG and required corticosteroids and immunosuppressive therapy to control her disease. Her father developed predominantly bulbar symptoms of MG at age 75 years. He died of complications experienced following a gastrostomy placed for continued difficulty swallowing. His brother developed similar symptoms of MG in his early 60s and died shortly after thymectomy. A 46-year-old nephew of the index case is also beginning to exhibit signs of generalized MG. Acetylcholine receptor antibodies were strongly positive in the index case and her nephew. (The assay was not available for her father and uncle). CONCLUSIONS: Four individuals in three successive generations had diagnoses of autoimmune MG. Study of familial cases such as these may clarify the contribution of genetic factors to the development of this disease.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".