Pregnancy Outcomes in Marfan Syndrome: A Retrospective Cohort Study
Bibliographic record
Abstract
OBJECTIVE: Marfan syndrome (MFS) is a rare connective tissue disease with significant risk for adverse cardiovascular outcomes. Our objective was to evaluate pregnancy and cardiovascular outcomes in pregnant women with MFS. STUDY DESIGN: We conducted a population-based retrospective cohort study using the Healthcare Cost and Utilization Project Nationwide Inpatient Sample (HCUP NIS) database from 2003 to 2010. We used unconditional regression analyses to compare maternal and fetal outcomes among pregnancies in women with and without MFS. RESULTS: Out of the 7,094,400 births in our cohort, 339 deliveries were to women with MFS. There was one maternal death and six aortic dissections among women with MFS. Births to women with MFS were more likely to be premature, odds ratio (OR) 2.15 (1.60-2.89), have intrauterine growth restricted and small for gestational age infants, OR 2.06 (1.24-3.43). Women with MFS were more likely to deliver by cesarean section, OR 1.91 (1.53-2.38) and were at higher risk of major morbidities including cardiac arrhythmias, OR 10.64 (5.49-20.61) and pneumothorax, OR 51.95 (6.18, 437.10). CONCLUSION: Pregnant women with MFS are at a particularly high risk of adverse pregnancy and cardiovascular events. Preconception counseling should take these risks into consideration and appropriate pregnancy care in tertiary centers should be considered.
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".