NIH workshop on human milk composition: summary and visions
Bibliographic record
Abstract
Nationally representative data from mother-child dyads that capture human milk composition (HMC) and associated health outcomes are important for advancing the evidence to inform federal nutrition and related health programs, policies, and consumer information across the governments in the United States and Canada as well as in nongovernment sectors. In response to identified gaps in knowledge, the National Institute of Diabetes and Digestive and Kidney Diseases of the NIH sponsored the "Workshop on Human Milk Composition-Biological, Environmental, Nutritional, and Methodological Considerations" held 16-17 November 2017 in Bethesda, Maryland. Through presentations and discussions, the workshop aimed to 1) share knowledge on the scientific need for data on HMC; 2) explore the current understanding of factors affecting HMC; 3) identify methodological challenges in human milk (HM) collection, storage, and analysis; and 4) develop a vision for a research program to develop an HMC data repository and database. The 4 workshop sessions included 1) perspectives from both federal agencies and nonfederal academic experts, articulating scientific needs for data on HMC that could lead to new research findings and programmatic advances to support public health; 2) information about the factors that influence lactation and/or HMC; 3) considerations for data quality, including addressing sampling strategies and the complexities in standardizing collection, storage, and analyses of HM; and 4) insights on how existing research programs and databases can inform potential visions for HMC initiatives. The general consensus from the workshop is that the limited scope of HM research initiatives has led to a lack of robust estimates of the composition and volume of HM consumed and, consequently, missed opportunities to improve maternal and infant health.
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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 | 0.012 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".