Infant and young child feeding interventions targeting overweight and obesity: A narrative review
Bibliographic record
Abstract
findings from systematic reviews into infant feeding and later adiposity are largely negative. World Health Organization (WHO) is auspicing Healthy Life Trajectories Initiative (HeLTI), a suite of trials aiming to prevent overweight/obesity in childhood. To inform planning, this narrative review sought to detail potentially effective components of nutrition-related interventions involving children aged 0 to 2 years. Systematic searches of PubMed and the Cochrane Library (2006-2016) identified 108 systematic reviews. These included 31 randomized trials in the age group of interest. Of these, 11 reported greater than or equal to 1 statistically significant (P < 0.05) benefit on body weight and/or composition. Six multicomponent trials whose interventions incorporated education to promote breastfeeding (four trials), responsive feeding (two trials), and healthy diet (eg, increasing fruit and vegetables and limiting unhealthy snack foods; five trials), delivered through home visits or at baby health clinics, reported relative reductions in body mass index (BMI) at the end of intervention. Early benefits were not maintained in the two trials reporting follow-up 1 to 3 years later. Other potentially effective approaches included lower protein formulas in formula-fed infants and education around reducing sugar-sweetened beverages. There is some evidence that infant feeding interventions can have a transient positive impact on a child's BMI. It is not known whether ongoing intervention can avoid the subsequent expected wash-out.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".