Pre-Pregnancy Obesity and Infants’ Motor Development within the First Twelve Months of Life: Who Is Expected to Be the Ultimate Carrier of the Obesity Burden?
Bibliographic record
Abstract
Introduction: Pre-pregnancy obesity is a significant public health concern with profound implications for maternal and child health. The burgeoning evidence suggests that maternal obesity prior to conception is intricately linked with an increased risk of gestational complications, as well as with adverse neonatal outcomes. Furthermore, the long and short-term health of offspring, including the risk of early motor development impairment, obesity, and metabolic syndrome in childhood and adulthood, may be adversely affected as well. Addressing pre-pregnancy obesity is critical for improving overall maternal and child health outcomes, and therefore, the aim of this study was to evaluate the connections linking pre-pregnancy obesity with infants’ motor development within the first twelve months of infants’ lives. Material and Methods: This study included 200 mother–infant pairs divided into two groups based on their pre-pregnancy body mass index values. To assess infants’ early motor development, we used the Alberta Infant Motor Scale (AIMS) and evaluated the parameters of infants’ early motor development at the ages of three, six, nine, and twelve months. Results: Pre-pregnancy overweight/obesity was significantly associated with excessive gestational weight gain (p < 0.001), fetal macrosomia (p = 0.022), and a family history of diabetes and cardiovascular diseases (p = 0.048 and p = 0.041, respectively), as well as with all observed parameters of early motor development at the ages of three, six, nine, and twelve months: AIMS 3 months total (p < 0.001), AIMS 6 months total (p < 0.001), AIMS 9 months total (p < 0.001), and AIMS 12 months total (p < 0.001). Furthermore, pre-pregnancy overweight/obesity was a significant predictor for AIMS 6 months total (p = 0.043) and AIMS 6 months supination (p = 0.017). Conclusions: Pre-pregnancy obesity is a critical determinant of pregnancy outcomes and offspring early motor development, with possible far-reaching implications for children’s long-term well-being. Addressing this issue requires a comprehensive approach that includes preconception weight management, targeted interventions during the pregnancy and postpartum periods, and ongoing research to better understand the underlying mechanisms and develop effective strategies for prevention and management.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".