Myasthenia Gravis in Pregnancy: Case Series and Systematic Review
Bibliographic record
Abstract
Background: Myasthenia gravis (MG) is an autoimmune disease which can affect reproductive-aged women and impact pregnancy outcomes. Objectives: To systematically review pregnancy outcomes for patients with MG. Search Strategy: Ovid MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials, PubMed (Non-Medline records), Web of Science, and LILACS databases were systematically searched for pregnancies complicated by MG. Selection Criteria: Human studies of five or more subjects reporting outcomes of MG in pregnancy published in any language. Data Collection and Analysis: Following identification and review of relevant studies, data on study characteristics, MG subtypes, treatment, disease and pregnancy outcomes were extracted. Assessment of bias was performed using the National Institutes of Health Quality Assessment Tool for Case Series. In addition, cases of MG in pregnancy managed at our centre were identified and outcomes included in the analysis. Main Results: In total, 32 publications met inclusion criteria for systematic review, for a total of 33 unique data sets including 48 cases at our institution. In total, outcome data was available for 824 pregnancies complicated by MG. Overall risk of MG exacerbation was 33.8% with a 6.4% risk of myasthenic crisis in pregnancy and 8.2% postpartum. Spontaneous vaginal delivery occurred in 56.3% of pregnancies. The risk of transient neonatal myasthenia gravis (TNMG) was 13.0%. Conclusions: One third of pregnant MG patients will experience an exacerbation with 6.4% and 8.2% experiencing myasthenic crisis in pregnancy and postpartum respectively. More than half of MG patients had a spontaneous vaginal birth. The risk of TNMG is 13%
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".