Maternal and newborn outcomes in pregnancies complicated by Guillain-Barré syndrome
Bibliographic record
Abstract
OBJECTIVES: Guillain-Barré syndrome (GBS) is a rare autoimmune disorder that affects the peripheral nervous system. The purpose of our study was to evaluate maternal and fetal/neonatal outcomes among pregnancies complicated by GBS. METHODS: We performed a retrospective cohort study using the Healthcare Cost and Utilization Project - National Inpatient Sample from the United States. ICD-9 codes were used to identify all pregnant women who delivered between 1999 and 2015 and had a diagnosis of GBS. The remaining women without GBS who delivered during that time period constituted the comparison group. The associations between maternal GBS and obstetrical and fetal/neonatal outcomes were evaluated using multivariate logistic regression, while adjusting for the confounding effects of maternal characteristics. RESULTS: 0.02). Further, women with GBS were more likely to have pregnancies complicated by preeclampsia, OR 1.69 (95 % CI 1.06-2.69), sepsis, 9.30 (2.33-37.17), postpartum hemorrhage, 1.83 (1.07-3.14), and to require a transfusion, 4.39 (2.39-8.05). They were also at greater risk of caesarean delivery, 2.07 (1.58-2.72) and increased length of hospital stay, 4.48 (3.00-6.69). Newborns of women with GBS were more likely to be growth restricted, 2.50 (1.48-4.23). CONCLUSIONS: GBS in pregnancy is associated with maternal and newborn adverse outcomes. These patients would benefit from close follow-up throughout their pregnancy and in the postpartum period.
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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 | 0.001 | 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".