Obesity Prevalence and Its Impact on Maternal and Neonatal Outcomes in Pregnant Women: A Systematic Review
Bibliographic record
Abstract
Globally, obesity prevalence has progressively increased and is now at epidemic levels; this trend is mirrored in women of childbearing age. There is a high level of evidence that maternal obesity is associated with a range of adverse pregnancy complications and neonatal outcomes, such as hypertensive disorders of pregnancy, gestational diabetes mellitus (GDM), large for gestational age (LGA) fetuses, premature birth, stillbirth, cesarean section, and postpartum hemorrhage, among certain others. This systematic review aimed to comprehensively evaluate the relationship between maternal obesity and health outcomes for both mothers and infants. The inclusion criteria encompass studies focusing on pregnant women with obesity, research examining obesity prevalence in pregnancy, and investigations into various maternal and neonatal outcomes. Quality assessment was performed using the Newcastle-Ottawa Scale to ensure the reliability and validity of findings, while meta-analysis was performed to calculate the pooled prevalence of obesity. The findings highlight significant associations between maternal obesity and adverse outcomes for both mothers and neonates, respectively. Increased gestational weight gain in obese individuals correlates with a higher risk of complications, such as cesarean delivery, preeclampsia, and postpartum hemorrhage. Specifically, obesity has been consistently linked to higher rates of GDM, which further elevates the likelihood of cesarean sections and other complications during labor. Additionally, in terms of neonatal outcomes, studies reveal that maternal obesity influences the incidence of LGA infants, often leading to macrosomia. Neonates born to obese mothers may also have increased rates of NICU admissions, reflecting the challenges posed by higher maternal weight and its associated risks. Maternal obesity is consistently associated with adverse maternal and neonatal outcomes. However, diversity in outcomes, such as Apgar scores, underscores the need for further research to better understand these complex relationships.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".