The Influence of Increased Prepregnancy Body Mass Index and Excessive Gestational Weight Gain on Pregnancy Course and Fetal and Maternal Perinatal Outcomes
Bibliographic record
Abstract
Background: The aim of our study was to assess the influence of prepregnancy Body Mass Index (BMI) changes as well as excessive gestational weight gain (GWG) on maternal and fetal perinatal parameters. Furthermore, we aimed to analyze the influence of increased prepregnancy BMI values and excessive GWG on neonatal early motoric development. Methods: The 203 eligible female participants were evaluated. Prepregnancy Body Mass Index (BMI) and excessive gestational weight gain (GWG) defined according to Institute of Medicine 2009 guidelines in the USA were assessed with tested maternal and fetal perinatal parameters and infants early motoric development (Alberta Infant Motor Scale—AIMS). Results: Significant predictors of increased prepregnancy BMI in perinatal period include: weight at delivery (p = 0.001), GWG (p = 0.002) and BMI at delivery (p < 0.001), while significant predictors of excessive GWG in perinatal period are: prepregnancy BMI (p = 0.029) and BMI at delivery (p < 0.001). In the group of participants with both increased prepregnancy BMI and excessive GWG versus others, significant predictors were: hypertension (HTA) (p = 0.019), amniotic fluid index (AFI) (p = 0.047), Pronation (AIMS) (p = 0.028) and Supination (AIMS) (p = 0.029). Conclusion: Increased prepregnancy BMI and excessive GWG are significantly associated with numerous perinatal factors that could alter the pregnancy course, pregnancy outcome and early motoric development of newborn. Moreover, increased prepregnancy BMI is shown to be a significant predictor of excessive GWG; thus, early selection of pregnant women for close monitoring of weight gain during pregnancy will have positive effects on reducing the risk of less favorable pregnancy course and early motoric development of newborn.
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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".