An Updated Evidence Scan of the Nutrient Composition of Human Milk in the United States and Canada: A Systematic Scoping Review
Bibliographic record
Abstract
In 2018, authors from USDA’s Agriculture Research Service (ARS) published a literature review (2) that summarized current knowledge of human milk nutrient composition in the United States. This comprehensive literature review captured studies published from 1980 to 2017, that were conducted in the United States and Canada. The review included 28 articles that reported on human milk composition of macronutrients and micronutrients. Most of the 28 articles were published before 1990 and mainly examined samples from small groups of generally healthy lactating women, with the majority not describing race/ethnicity. Wu et al. (2018) concluded that 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, Magnesium, Potassium, etc.); data for 7–12 mo postpartum and for other nutrients are very scarce (e.g., iodine). This proposed project aims to conduct an evidence scan to update the literature review conducted by ARS, using a systematic scoping approach. An updated the literature search will be conducted to describe the evidence on the nutrient composition and volume of mature milk (i.e., from 3 weeks postpartum, to 12 months and beyond), published from 2017 to 2022. A draft analytic framework (Figure 1) and draft inclusion and exclusion criteria (Table 1) are provided in this registration. References 1. Casey CE, Hambidge KM. Nutritional aspects of human lactation. In:Neville MC, Neifert MR, editors. Lactation: physiology, nutrition, and breast-feeding. New York: Plenum Press, 1983. p. 199–248. 2. Wu X, Jackson RT, Khan SA, Ahuja J, Pehrsson PR. Human milk nutrient composition in the United States: current knowledge, challenges, and research needs. Curr Dev Nutr. 2018;2:nzy025.
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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 | 0.040 | 0.137 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.043 | 0.044 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".