Office-Based Intervention to Reduce Bottle Use Among Toddlers: TARGet Kids! Pragmatic, Randomized Trial
Bibliographic record
Abstract
OBJECTIVE: The goal was to determine whether an office-based, educational intervention for parents of 9-month-old children could reduce bottle use and iron depletion at 2 years of age. METHODS: Between January 2006 and 2007, 251 healthy, 9-month-old infants attending a routine health maintenance visit were assigned randomly to intervention or control groups. Parents in the intervention group were introduced to a 1-week protocol to wean their children from the bottle. Iron depletion (ferritin levels of <10 microg/L) and bottle use at 2 years were assessed. RESULTS: A total of 201 children were monitored to 2 years of age (follow-up rate: 81%). Rates of iron depletion (10 [10%] of 102 children vs 13 [13%] of 99 children; P = .42) and milk consumption of >16 oz (16 [16%] of 102 children vs 17 [17%] of 99 children; P = .7) were not significantly different between the 2 groups at 2 years of age. However, children in the intervention group started using a cup 3 months earlier (9 vs 12 months; P = .001), were weaned from the bottle 4 months earlier (12 vs 16 months; P = .004), and were more than one-half as likely to be using a bottle at 2 years of age (15 [15%] of 102 children vs 39 [40%] of 99 children; P = .0004). CONCLUSIONS: This simple intervention administered during a health maintenance visit did not result in a decrease in iron depletion at 2 years of age but did result in a 60% reduction in prolonged bottle use.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| 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) | 0.006 | 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".