Myasthenia Gravis and Pregnancy: Toronto Specialty Center Experience
Bibliographic record
Abstract
BACKGROUND: Myasthenia gravis (MG) is an autoimmune disorder that frequently affects young women of reproductive age. The multidirectional interplay between MG, pregnancy, and fetal health poses a complex scenario for pregnant women with MG and the healthcare team. Here, we reviewed our local experience with MG, pregnancy, and outcomes. METHODS: We performed a retrospective chart review of patients with MG attending the Prosserman Family Neuromuscular Clinic from 2001 to 2019 and who were referred to a high-risk pregnancy clinic. MG status was defined as stable, better, or worse. Information was collected on the delivery route, pregnancy, and neonatal complications. RESULTS: We identified 20 women with MG for a total of 28 pregnancies. Worsening was observed in 50% of pregnancies: 18% during pregnancy, 25% following delivery, and 7% during both. 66.7% of patients with MG duration of 2 years or less had worsening during pregnancy. Three patients who stopped immunosuppressive treatment during pregnancy worsened and one had a crisis. C-section was done in 29% of pregnancies. The rate of delivery complications was 7% and of neonatal MG was 7%. CONCLUSION: A high proportion of MG patients worsened during pregnancy, particularly those with disease duration less than 2 years, and those who discontinued immunosuppression during pregnancy. However, pregnancy was largely unaffected, rate of neonatal MG was low, frequencies of C-section, delivery complications, and premature births were similar to the general population. While the study has limitations due to the retrospective nature, these insights provide some guidance when counseling young myasthenic women about family planning.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 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".