Maternal and neonatal outcomes among pregnant women with myasthenia gravis
Bibliographic record
Abstract
Objectives Myasthenia gravis (MG) is an autoimmune disease affecting the neuromuscular junction marked by weakness and fatiguability of skeletal muscle. MG has an unpredictable course in pregnancy. Our purpose was to evaluate the effect of MG on maternal and neonatal outcomes. Methods Using the United States' Healthcare Cost and Utilization Project Nationwide Inpatient Sample from 2005 to 2015, we conducted a retrospective cohort study consisting of women who delivered during that period. Multivariate logistic regression models, adjusted for baseline maternal demographics and comorbidities, were used to compare maternal and neonatal outcomes among pregnancies in women with and without MG. Results During the study period, 974 deliveries were to women diagnosed with MG. Women with MG were more likely to be older, African American, obese, have Medicare insurance and be discharged from an urban teaching hospital. Women with MG were also more likely to have chronic hypertension, pre-gestational diabetes, hypothyroidism, and chronic steroid use. Women with MG were at greater risk for acute respiratory failure (OR 13.7, 95% CI 8.9-21.2) and increased length of hospital stay (OR 2.5, 95% CI 1.9-3.3). No significant difference was observed in the risk of preterm premature rupture of membranes, caesarean section or instrumental vaginal delivery. Neonates of women with MG were more likely to be premature (OR 1.4, 95% CI 1.2-1.8). Conclusions MG in pregnancy is a high-risk condition associated with greater risk of maternal respiratory failure and preterm birth. Management in a tertiary care center with obstetrical, neurological, anesthesia and neonatology collaboration is recommended.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".