The impact of donating milk on the health of milk donors and their infants: A systematic review and meta‐analysis protocol
Bibliographic record
Abstract
Objectives: Breast milk is the recommended nutritional source for newborns and has been associated with decreased morbidity in low-birth-weight and preterm infants. In situations where breast milk is not available, donor breast milk is an alternative. Milk banking is becoming increasingly common worldwide to meet this need. Although the benefits of donor breast milk for the recipient infant are well established, the health impact on the breast milk donor and the infant of the breast milk donor is an area of current research. We aim to synthesize and evaluate the available evidence regarding the impact of donating breast milk on the health, lactation, and well-being of the breast milk donor, and the health and growth of the infant of the breast milk donor. Methods: We will search electronic databases, grey literature, and the websites of relevant international organizations. We will include studies that involve lactating women and their infants, healthy or with health conditions, who donate breast milk, without restrictions on study date, language, or study design. If sufficient homogeneity exists between studies, we will complete meta-analyses. We will evaluate the risk of bias using the Risk of Bias tool or the Cochrane Risk of Bias in Non-Randomized Studies tool. We will evaluate the overall certainty of the evidence using the Grading of Recommendations Assessment, Development, and Evaluation approach. Conclusion: In this systematic review and meta-analysis, we will summarize the current literature regarding the effects of human milk donation on human milk donors and their infants.
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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 | 0.044 | 0.075 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.026 | 0.031 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.040 | 0.003 |
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".