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Record W4200274029 · doi:10.2471/blt.21.286943

Developing global guidance on human milk banking

2021· article· en· W4200274029 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBulletin of the World Health Organization · 2021
Typearticle
Languageen
FieldMedicine
TopicBreastfeeding Practices and Influences
Canadian institutionsSinai Health SystemUniversity of Toronto
FundersWorld Health Organization
KeywordsBusinessDeveloping countryMedicineComputer scienceBiology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.884
Threshold uncertainty score0.972

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.039
GPT teacher head0.348
Teacher spread0.309 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it