Maternal, pregnancy, and neonatal outcomes for women with Turner syndrome
Bibliographic record
Abstract
BACKGROUND: Marfan syndrome (MFS) is an autosomal dominant hereditary disorder which affects cardiovascular structure and function. With medical advances, more women with MFS experience pregnancy, which may increase maternal and neonatal risk. Existing research has been limited by small or clinical samples. This study examines the association of MFS and adverse maternal, neonatal, and obstetric outcomes. METHODS: We conducted a cross-sectional study using the discharge abstract database, containing all labor and delivery hospitalizations in Canada (excluding Quebec) from fiscal years 2004-2015 where women delivered a live- or stillbirth. We measured maternal and neonatal morbidity, preterm births (<37 weeks), small-for-gestational-age births, perinatal mortality, and adverse maternal cardiovascular events. For each outcome, we calculated the absolute risk for women with and without MFS and used generalized estimating equations with a logit function to calculate odds. RESULTS: Overall, 2,682,461 women delivered a live or stillborn infant in Canada during the study period, with 135 birth events to women with MFS. Women with MFS did not have significantly higher odds of severe maternal morbidity during their delivery (aOR:1.3; 95%CI: 0.4-4.0). Similarly, their infants did not have significantly higher odds of neonatal morbidity. However, infants born to women with MFS were significantly more likely to be born preterm (aOR:2.6; 95%CI: 1.6-4.3) and to be small-for-gestational-age (aOR:1.8; 95%CI:1.0-3.1). CONCLUSIONS: This population-based study indicates that, although some women with MFS may experience higher odds of maternal and/or neonatal morbidity during labor and delivery, the majority of women with MFS can have healthy births with proper clinical management.
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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 | 0.001 | 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) | 0.001 | 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".