1419-P: Pregravid Metabolic Syndrome and Risk of Adverse Outcomes in Pregnancy: A Preconception Cohort Study
Bibliographic record
Abstract
Women with a history of adverse outcomes in pregnancy (including pre-term birth, delivery of a small-for-gestational-age (SGA) infant, preeclampsia, and gestational diabetes (GDM)) have a higher prevalence of metabolic syndrome (MetS) and cardiovascular disease, as compared to their peers. However, it is not known if MetS precedes the index pregnancy in women who develop these outcomes. Thus, we sought to evaluate the impact of pre-gravid MetS on the risk of adverse outcomes in pregnancy. In this prospective pre-conception cohort study, 1183 newly-married women underwent systematic assessment of cardiovascular risk factors (anthropometry, blood pressure, lipids, glucose) and then were followed across a subsequent pregnancy for the outcomes of interest. The women were stratified into two groups based on the presence (n=49) or absence (n=1134) of pre-gravid MetS (harmonized definition). The groups did not differ in length of gestation (p=0.31) or infant birthweight (p=0.21). Of note, women with pre-gravid MetS were more likely to have a Caesarean delivery (61.4% vs. 38.6%, p=0.003). However, there were no differences between the groups in the incidence of pre-term delivery, SGA, LGA, preeclampsia or GDM (Table). In conclusion, the increased lifetime risk of MetS observed in women with a history of these adverse pregnancy outcomes does not necessarily manifest prior to their pregnancy. Disclosure R. Retnakaran: Consultant; Self; Eli Lilly and Company, Novo Nordisk Inc., Sanofi. Research Support; Self; Boehringer Ingelheim International GmbH, Novo Nordisk Inc. S. Wen: None. H. Tan: None. C. Ye: None. M. Shen: None. G.N. Smith: None. M.C. Walker: None. Funding Canadian Institutes of Health Research
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".