Effect of expressed human milk feeding on breastfeeding duration in term infants: A systematic review and meta‐analysis
Bibliographic record
Abstract
BACKGROUND: The mode of breastfeeding is evolving, with an increasing trend of expressed human milk feeding. However, previous studies that examined the association between expressed human milk feeding and breastfeeding duration showed inconsistent findings. OBJECTIVE: To understand the association between any and only expressed human milk feeding and breastfeeding duration and to describe the prevalence of expressed human milk feeding among parents of healthy term infants. SEARCH STRATEGY: We systematically searched CINAHL, EMBASE, PubMed, PsycINFO, Cochrane Library, and Google Scholar, up to December 2023. SELECTION CRITERIA: Observational studies written in English and which reported at least one of the intended incomes mentioned were included. DATA COLLECTION AND ANALYSIS: statistic. MAIN RESULTS: Any expressed human milk feeding within 3 months postpartum (HR 1.34, 95% CI 1.04-1.73) increased the risk of breastfeeding cessation. Only expressed human milk feeding within 3 months and ≥3 months postpartum were associated with 43% and 81% increased risk of breastfeeding cessation, respectively. Furthermore, 81% of breastfeeding persons had ever expressed human milk feeding. Only seven out of 31 studies were rated as good quality. CONCLUSION: To date, only a few recent high-quality studies have explored the prevalence of expressed human milk feeding and its association with breastfeeding duration among parents of healthy term infants. Further high-quality research is required to investigate these aspects further.
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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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.021 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".