Breastfeeding and Infant Size: Evidence of Reverse Causality
Bibliographic record
Abstract
Infants who receive prolonged and exclusive breastfeeding grow more slowly during the first year of life than those who do not. However, infant feeding and growth are dynamic processes in which feeding may affect growth, and prior growth and size may also influence subsequent feeding decisions. The authors carried out an observational analysis of 17,046 Belarusian infants who were recruited between June 1996 and December 1997 and who participated in a cluster-randomized trial of a breastfeeding promotion intervention. To assess the effects of infant size on subsequent feeding, the authors restricted the analysis to infants breastfed (or exclusively breastfed) at the beginning of each follow-up interval and examined associations between weight or length at the beginning of the interval and weaning or discontinuation of exclusive breastfeeding by the end of the interval. Smaller size (especially weight for age) was strongly and statistically significantly associated with increased risks of subsequent weaning and of discontinuing exclusive breastfeeding (adjusted odds ratios = 1.2-1.6), especially between 2 and 6 months, even after adjusment for potential confounding factors and clustered measurement. The authors speculate that similar dynamic processes involving infant crying, other signs of hunger, and supplementation/weaning undermine causal inferences about the "effect" of prolonged and exclusive breastfeeding on slower infant growth.
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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 | 0.052 | 0.233 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".