Human Milk Nutrient Composition in the United States: Current Knowledge, Challenges, and Research Needs
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.
Bibliographic record
Abstract
Human milk is considered to be the ideal food for infants. Accurate, representative, and up-to-date nutrient composition data of human milk are crucial for the management of infant feeding, assessment of infant and maternal nutritional needs, and as a guide for developing infant formula. Currently in the United States, the nutrient profiles of human milk can be found in the USDA National Nutrient Database for Standard Reference, and in books or review articles. Nonetheless, these resources all suffer major drawbacks, such as being outdated, incomplete profiles, limited sources of data, and uncertain data quality. Furthermore, no nutrient profile was developed specifically for the US population. The purposes of this review were to summarize the current knowledge of human milk nutrient composition from studies conducted in the United States and Canada, and to identify the knowledge gaps and research needs. The literature review was conducted to cover the years 1980-2017, and 28 research papers were found containing original data on macronutrients and micronutrients. Most of these 28 studies were published before 1990 and mainly examined samples from small groups of generally healthy lactating women. The experimental designs, including sampling, storage, and analytic methods, varied substantially between the different studies. Data of several components from these 28 studies showed some consistency for 1-6 mo postpartum, especially for protein, fat, lactose, energy, and certain minerals (e.g., calcium). The data for 7-12 mo postpartum and for other nutrients are very scarce. Comprehensive studies are required to provide current and complete nutrient information on human milk in the United States.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it