Practices, predictors and consequences of expressed breast-milk feeding in healthy full-term infants
Bibliographic record
Abstract
OBJECTIVE: To investigate the prevalence and predictors of expressed breast-milk feeding in healthy full-term infants and its association with total duration of breast-milk feeding. DESIGN: Prospective cohort study. SETTING: In-patient postnatal units of four public hospitals in Hong Kong. SUBJECTS: A total of 2450 mother-infant pairs were recruited in 2006-2007 and 2011-2012 and followed up prospectively for 12 months or until breast-milk feeding had stopped. RESULTS: Across the first 6 months postpartum, the rate of exclusive expressed breast-milk feeding ranged from 5·1 to 8·0 % in 2006-2007 and from 18·0 to 19·8 % in 2011-2012. Factors associated with higher rate of exclusive expressed breast-milk feeding included supplementation with infant formula, lack of previous breast-milk feeding experience, having a planned caesarean section delivery and returning to work postpartum. Exclusive expressed breast-milk feeding was associated with an increased risk of early breast-milk feeding cessation when compared with direct feeding at the breast. The hazard ratio (95 % CI) ranged from 1·25 (1·04, 1·51) to 1·91 (1·34, 2·73) across the first 6 months. CONCLUSIONS: Mothers of healthy term infants should be encouraged and supported to feed directly at the breast. Exclusive expressed breast-milk feeding should be recommended only when medically necessary and not as a substitute for feeding directly at the breast. Further research is required to explore mothers' reasons for exclusive expressed breast-milk feeding and to identify the health outcomes associated with this practice.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".