Association of Gestational Hypertension and Eclampsia to Maternal Smoking by Pre-Pregnancy Body Mass Index Status Among Aged 20-29-Years in the United States
Bibliographic record
Abstract
Objective To investigate whether gestational hypertension (GH) and/or eclampsia was associated with the timing of maternal smoking when stratified by pre-pregnancy body mass index (pBMI) status. Study Design and Methods 1,376,271 US-born mothers aged 20-29 from the 2019 infant natality data who had a singleton birth (20+ weeks of gestation) were analyzed in this study. Maternal smoking status was defined into five groups, i.e., non-smokers, quitted smoking before pregnancy, quitted smoking before the 2nd trimester, quitted smoking before the 3rd trimester, and smoked whole-time. Odds ratios (ORs) of GH or eclampsia were estimated separately using multiple logistic regression for maternal smoking by pBMI status (kg/m2): underweight (<18.5), normal (18.5≤25.0), overweight (25.0≤30.0), and obese (≥30.0). Results Compared to non-smokers, the adjusted ORs (95% CIs) of GH for mothers who quit before pregnancy with pBMI underweight, normal, overweight, and obese were 1.17 (0.92-1.49), 1.11 (1.03-1.19), 1.13 (1.05-1.22), 1.13 (1.08-1.19), respectively. While the ORs (95% CIs) of GH for mothers who smoked for the entirety of their pregnancy were 0.71 (0.60-0.84), 0.80 (0.75-0.84), 0.79 (0.74-0.84), and 0.82 (0.78-0.85), respectively. The adjusted ORs for eclampsia showed a different pattern, only that for mothers who smoked for their whole pregnancy with normal and obese showed significantly (0.69 (0.53-0.91) for normal weight, 0.73 (0.58-0.92) for obese). Conclusion In comparison to non-smokers, an increase in the odds of GH were observed amongst normal, overweight, and obese mothers quitting before pregnancy meanwhile a decreased odds were observed amongst mothers smoking throughout pregnancy in all pBMI classes.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| 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".