Age of cow milk introduction and growth among 3–5-year-old children
Bibliographic record
Abstract
OBJECTIVE: To evaluate the association between the age of cow milk introduction and childhood growth. DESIGN: A secondary analysis of a prospective cohort study. SETTING: Toronto, Canada. PARTICIPANTS: Healthy children <5 years of age enrolled in the TARGet Kids! practice-based research network. The primary exposure was the age of cow milk introduction. The primary outcome was height-for-age z-score. Secondary outcomes were volume of cow milk consumed (cups/d) and BMI z-score. Outcomes were measured at the last visit before 5 years of age. Multiple linear regression was used to examine these relationships. RESULTS: Among 1981 children, introduction of cow milk at a younger age was associated with greater height by 3-5 years of age (P < 0·001). Each month earlier that cow milk was introduced was associated with 0·03 higher height-for-age z-score unit (95 % CI -0·05, -0·02) or 0·1 cm (95 % CI -0·15, -0·12 cm). At 4 years of age, the height difference between a child introduced to cow milk at 9 v. 12 months was 0·4 cm (95 % CI -0·45, -0·35 cm). There was no association between the timing of cow milk introduction and volume of cow milk consumed per day or BMI z-score. CONCLUSIONS: Earlier introduction of cow milk was associated with greater height but not with weight status in children aged 3-5 years. Further research is needed to understand the casual relationship between earlier cow milk consumption and childhood height. These findings may be important for paediatricians and parents when considering when to introduce cow milk.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".