Risk Factors for Maternal Body Mass Index and Gestational Weight Gain in Twin Pregnancies
Bibliographic record
Abstract
Abstract Objective This retrospective cohort study analyzes risk factors for abnormal pre-pregnancy body mass index and abnormal gestational weight gain in twin pregnancies. Methods Data from 10 603/13 682 twin pregnancies were analyzed using uni- and multivariable logistic regression models to determine risk factors for abnormal body mass index and weight gain in pregnancy. Results Multiparity was associated with pre-existing obesity in twin pregnancies (aOR: 3.78, 95% CI: 2.71 – 5.27). Working in academic or leadership positions (aOR: 0.57, 95% CI: 0.45 – 0.72) and advanced maternal age (aOR: 0.96, 95% CI: 0.95 – 0.98) were negatively associated with maternal obesity. Advanced maternal age was associated with a lower risk for maternal underweight (aOR: 0.95, 95% CI: 0.92 – 0.99). Unexpectedly, advanced maternal age (aOR: 0.98, 95% CI: 0.96 – 0.99) and multiparity (aOR: 0.6, 95% CI: 0.41 – 0.88) were also associated with lower risks for high gestational weight gain. Pre-existing maternal underweight (aOR: 1.55, 95% CI: 1.07 – 2.24), overweight (aOR: 1.61, 95% CI: 1.39 – 1.86), obesity (aOR: 3.09, 95% CI: 2.62 – 3.65) and multiparity (aOR: 1.64, 95% CI: 1.23 – 2.18) were all associated with low weight gain. Women working as employees (aOR: 0.85, 95% CI: 0.73 – 0.98) or in academic or leadership positions were less likely to have a low gestational weight gain (aOR: 0.77, 95% CI: 0.64 – 0.93). Conclusion Risk factors for abnormal body mass index and gestational weight gain specified for twin pregnancies are relevant to identify pregnancies with increased risks for poor maternal or neonatal outcome and to improve their counselling. Only then, targeted interventional studies in twin pregnancies which are desperately needed can be performed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".