Maternal BMI, breastfeeding and perinatal factors that influence early childhood growth trajectories: a scoping review
Bibliographic record
Abstract
Obesity rates among children are rapidly rising internationally and have been linked to noncommunicable diseases in adulthood. Individual preventive strategies have not effectively reduced global obesity rates, leading to a gap in clinical services regarding the development of early perinatal interventions. The objective of this scoping review is to explore the relationship between maternal BMI and breastfeeding behaviors on child growth trajectories to determine their relevance in developing interventions aimed at preventing childhood obesity.The scoping review was guided and informed by the Arksey and O'Malley (2005) framework. A systematic search was performed in four databases. Studies included in the final review were collated and sorted into relevant themes. A systematic search yielded a total of 5831 records (MEDLINE: 1242, EMBASE: 2629, CINAHL: 820, PubMed: 1140). Results without duplicates (n = 4190) were screened based on relevancy of which 197 relevant-full-text articles were retrieved and assessed for eligibility resulting in 14 studies meeting the inclusion criteria. Data were extracted and charted for the studies and six themes were identified: (1) healthy behaviors, lifestyle, and social economic status; (2) parental anthropometrics and perinatal weight status; (3) genetics, epigenetics, and fetal programming; (4) early infant feeding; (5) infant growth trajectories; and (6) targeted prevention and interventions. Early life risk factors for child obesity are multifactorial and potentially modifiable. Several at-risk groups were identified who would benefit from early preventative interventions targeting the importance of healthy weight gain, exclusive breastfeeding to 6 months, and healthy lifestyle behaviors.
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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 | 0.007 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".