Do Lipoid Metabolism Disorders Increase Risks of Maternal and Fetal Complications in Pregnancies? [12N]
Bibliographic record
Abstract
INTRODUCTION: The effects of lipoid metabolism disorders (LMD) are not well understood in pregnancy; thus, this study aims at understanding the different maternal and fetal outcomes that can be associated with this condition in pregnancy. METHODS: Using the Healthcare Cost and Utilization Project – National Inpatient Sample from the United States, a retrospective cohort study was conducted to determine the risks of complications in pregnant women known to have LMDs. All pregnant patients diagnosed with LMDs between 1999 and 2015 were identified using the International Classification of Disease-9 coding. Maternal and neonatal outcomes in pregnant women with LMDs were analyzed using multivariate logistic regression. RESULTS: A total of 13,792,544 pregnant women were included in our study; 9,666 women were diagnosed with LMDs for an overall prevalence of 7.0 per 10,000 births. Births to women with LMDs were more likely to have pregnancies complicated by diabetes and hypertension. Women with LMDs were more likely to have premature births, OR 1.42 (95% CI 1.35–1.53), suffer from myocardial infarctions, 11.97 (6.07–23.58), venous thromboembolisms, 2.03 (1.64–2.51), postpartum hemorrhage, 1.34 (1.21–1.48), and maternal death, 3.52 (1.97–6.32). There was also an increased risk of congenital anomalies, 2.48 (2.09–2.93), intrauterine growth restriction, 1.41 (1.26–1.58), and intrauterine fetal demise, 1.79 (1.51–2.13). CONCLUSION: The effects of LMDs on pregnancies are not very well understood, but women with these disorders appear to be at significantly increased risks of poor maternal and fetal outcomes. Better understanding of these risks and complications can allow for better outcomes in pregnant women suffering from lipoid metabolism disorders.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".