Developing global guidance on human milk banking
Bibliographic record
Abstract
Donor human milk is recommended by the World Health Organization both for its advantageous nutritional and biological properties when mother's own milk is not available and for its recognized support for lactation and breastfeeding when used appropriately. An increasing number of human milk banks are being established around the world, especially in low- and middle-income countries, to facilitate the collection, processing and distribution of donor human milk. In contrast to other medical products of human origin, however, there are no minimum quality, safety and ethical standards for donor human milk and no coordinating global body to inform national policies. We present the key issues impeding progress in human milk banking, including the lack of clear definitions or registries of products; issues around regulation, quality and safety; and ethical concerns about commercialization and potential exploitation of women. Recognizing that progress in human milk banking is limited by a lack of comparable evidence, we recommend further research in this field to fill the knowledge gaps and provide evidence-based guidance. We also highlight the need for optimal support for mothers to provide their own breastmilk and establish breastfeeding as soon as and wherever possible after birth.
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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 | 0.045 | 0.086 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.026 | 0.016 |
| Insufficient payload (model declined to judge) | 0.018 | 0.009 |
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".