Early Obesity Prevention: A Randomized Trial of a Practice-Based Intervention in 0–24-Month Infants
Bibliographic record
Abstract
OBJECTIVE: A pediatric office-based intervention was implemented following a randomized, controlled design, aimed at improving child feeding practices and growth patterns and ultimately reducing risk for overweight and obesity later in life. METHODS: Four clinics (232 infants) were randomized to control or intervention (I), the latter delivered by health care provider at each of 7-9 well-baby visits over 2 years, using a previously developed program (Growing Leaps and Bounds) that included verbal, visual, and text advice and information for parents. RESULTS: The I group offered significantly less soda (p = 0.006), sweetened tea (p = 0.01), punch (p = 0.02) and/or cow's milk (p = 0.001) to infants and delayed the introduction of drink/food other than breast milk (p < 0.05). Parents in the I group had a higher perceived parental monitoring (p = 0.05) and restriction (p = 0.01) on infant feeding. While the I group exhibited at baseline more adverse socioeconomic indicators than the control group, growth trajectory or body size indices did not significantly differ between groups. CONCLUSIONS: Education provided by health care providers in addition to follow-up monthly phone calls may help modify parental behaviors related to child feeding and increase parental sense of responsibility toward child eating 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".