Preconceptional Cardiovascular Health and Pregnancy Outcomes in Women with Systemic Lupus Erythematosus
Bibliographic record
Abstract
OBJECTIVE: To estimate the effects of preconceptional cardiovascular (CV) health, measured by American Heart Association (AHA) guidelines, on pregnancy outcomes in women with systemic lupus erythematosus (SLE). METHODS: The study included patients in the Hopkins Lupus Pregnancy Cohort. Body mass index (BMI), total cholesterol, and blood pressure (BP) in the most recent clinic visit prior to conception or first trimester were used to determine CV health (ideal, intermediate, or poor health) based on AHA definitions. Outcomes included preterm birth, gestational age at birth, and small for gestational age (SGA). Multivariable linear and logistic regression models with generalized estimating equations estimated the association of each CV health factor and outcome. RESULTS: The analysis included 309 live births. There were 95 preterm births (31%), and of the 293 pregnancies with birth weights, 18% were SGA. Ideal BMI, total cholesterol, and BP were reported in 56%, 85%, and 51% of pregnancies, respectively. Intermediate BMI was associated with decreased odds of SGA (OR 0.26, 95% CI 0.11-0.63), adjusted for race and prednisone use. Intermediate/poor total cholesterol was associated with increased odds of preterm birth (OR 2.21, 95% CI 1.06-4.62). Intermediate/poor BP was associated with decreased gestational age at birth (β -0.96, 95% CI -1.62 to -0.29). CONCLUSION: Poor/intermediate preconception CV health affects pregnancy outcomes of preterm birth and SGA infants among women with SLE. Efforts to maintain BMI, total cholesterol, and BP within the recommended ideal range prior to pregnancy is important to improve pregnancy outcomes in women with SLE.
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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 | 0.000 | 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 | 0.001 |
| 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".