Breast-Feeding and Health Consequences in Early Childhood: Is There an Impact of Time-Dependent Confounding
Bibliographic record
Abstract
BACKGROUND: Estimated effects of breast-feeding on childhood health vary between studies, possibly due to confounding by baseline maternal and child characteristics. Possible time-dependent confounding has received little consideration. Our aim was to evaluate the impact of such confounding. METHODS: We estimated the relationship between cumulative exclusive breast-feeding up to 6 months and wheezing, rash and body mass index (BMI) at 12 months [in the Whistler cohort (n = 494) and PROBIT (n = 11,463)], and wheezing, rash, asthma, hay fever, eczema, allergy and BMI at age 6.5 years (PROBIT). We adjusted for time-dependent confounding by weight, length, rash, respiratory illness and day care attendance using marginal structural models (MSMs). RESULTS: Weight and day care attendance appeared potential time-dependent confounders, since these predicted breast-feeding status and were influenced by previous breast-feeding. However, adjustment for time-dependent confounders did not markedly change the estimated associations. For example, in PROBIT the adjusted increase in BMI at 12 months per 1-month increase in exclusive breast-feeding was 0.04 (95% CI -0.09 to 0.01) using logistic regression and -0.06 (95% CI -0.11 to -0.01) using MSM. In Whistler, these estimates were each -0.05 (95% CI -0.10 to 0.00). CONCLUSIONS: In two cohort studies, there was little evidence of time-dependent confounding by weight, length, rash, respiratory illness or day care attendance of the effects of breast-feeding on early childhood health.
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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 | 0.073 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.008 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".