Maternal and Neonatal Outcomes Among Pregnant Women With Myasthenia Gravis [22O]
Bibliographic record
Abstract
INTRODUCTION: 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 Healthcare Cost and Utilization Project Nationwide Inpatient Sample from 1999 to 2015, we conducted a population-based retrospective cohort study consisting of women who delivered during that period. Multivariate logistic regression was used to compare maternal and neonatal outcomes among pregnancies in women with and without MG, while adjusting for baseline maternal characteristics. IRB approval was not required for this study. RESULTS: During the 17-year study period, 1087 deliveries were to women with MG. Women with MG were more likely to be older, Caucasian, obese, have Medicare insurance and be discharged from an urban teaching hospital. Women with MG were also more likely to be smokers, have chronic hypertension, pre-gestational diabetes, hypothyroidism and display chronic steroid use. Women with MG were at greater risk for acute respiratory failure (OR 15.7, 95% CI 10.6–23.3) and increased length of hospital stay (OR 2.6, 95% CI 2.0–3.3). No significant difference was observed in the risk of preterm premature rupture of membranes, cesarean section or instrumental vaginal delivery. Neonates of women with MG were also more likely to be premature (OR 1.4, 95% CI 1.1–1.6). CONCLUSION: MG in pregnancy is a high-risk pregnancy condition associated with adverse maternal and newborn outcomes. Management in a tertiary care center with obstetrical, neurological and anesthesia collaboration is recommended.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".