Gestational Weight Gain, Pregnancy Related Complications and the Short-Term Risks for the Offspring
Bibliographic record
Abstract
Background and objectives: Maternal obesity influences pregnancy course in several different manners, and imbalanced nutrition during pregnancy may lead to various adverse pregnancy outcomes. Additionally, nutritional status during pregnancy may have implications for the health of the offspring and may possibly influence early motor development in children. The aim of this study was to assess the impact of excessive gestational weight gain (EGWG) on pregnancy outcomes and infant’s motor development within the first twelve months of life. Materials and methods: The study included 200 participants divided in two groups based on their gestational weight gain. Maternal, perinatal, and neonatal factors were analyzed, and early motor development was assessed using the Alberta infant motor scale (AIMS). Results: EGWG was significantly associated with: pre-pregnancy BMI (p < 0.001), family history for cardiovascular diseases (p = 0.013) and diabetes mellitus (p = 0.045), hypertensive disorder of pregnancy (p = 0.003), gestational diabetes mellitus (p < 0.001), gestational anemia (p = 0.001), vitamin D deficiency (p = 0.001), metformin use (p = 0.045), pre-labor premature rupture of membranes (p = 0.031), amniotic fluid index (p = 0.047), and APGAR score in the first five min of life (p = 0.007). Scored by AIMS, EGWG was significantly associated with parameters of early motor development at the age of three AIMS total (p < 0.001), six AIMS total (p < 0.001), nine AIMS total (p < 0.001), and twelve AIMS total (p < 0.001) months of infant’s life. Conclusions: The link between EGWG and adverse neurodevelopmental outcomes in offspring is a complex and multifaceted issue. Our results imply significant alterations in early motor development in the group of infants born from mothers who gained weight excessively during pregnancy. Further studies are needed to unravel the intricacies of this relationship and inform strategies for preventive interventions and supportive care during pregnancy and infancy.
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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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".