Pregnancy planning may impact maternal and neonatal outcomes in people with myasthenia gravis
Bibliographic record
Abstract
INTRODUCTION: Myasthenia Gravis (MG) is an acquired autoimmune condition commonly diagnosed in young people of reproductive age resulting in neuromuscular junction dysfunction. The course of MG during pregnancy and its impact on maternal and neonatal outcomes is vary in the literature. Pregnancy planning is a known strategy and modifiable risk factor in obstetric practice to decrease maternal and neonatal morbidity. We aim to assess if planning a pregnancy impacts maternal and neonatal outcomes, MG exacerbation, and pregnancy-related complications. METHODS: This study utilized data from an online, North American survey entitled "A Patient Centered study on Pregnancy in People with Myasthenia Gravis", distributed with the assistance of MG advocacy groups in the United States and Canada. It included individuals with MG who had at least one pregnancy in the last 10-years. Key maternal and neonatal outcomes were compared between planned and unplanned pregnancies. RESULTS: Out of 156 survey participants, 58 had a pregnancy following MG diagnosis, totaling 90 reported pregnancies. Of these, 56 (62.2%) were planned and 34 (37.8%) were unplanned pregnancies. The unplanned pregnancies were associated with more MG exacerbations, hospitalizations, and intensive care unit admission (37.7% vs. 13.7%, 26.5% vs. 11%, and 17.6% vs. 8.9%, respectively, p ≤ .05). The neonatal outcomes did not significantly differ between the groups. DISCUSSION: Planned pregnancies in people with MG may be associated with a reduced gestational and post-partum risk of MG exacerbation, hospitalizations, and ICU admissions. Larger studies are required to confirm this association and account for potential contributing variables.
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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) | 0.000 | 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".