Comparison of mineral content in breast milk between early and later lactation periods among indigenous women from Western Highlands of Guatemala (623.11)
Bibliographic record
Abstract
Background : Human breast milk is the normal source of minerals for infant growth, development and metabolic function up to 6 months, but little information is available about how mineral concentrations vary throughout lactation in developing countries. Our objectives were to compare mineral concentrations between early (<45d) and later (4‐6mo) lactation periods, and to determine if mineral concentrations varied with infant age within these two lactation periods. Methods : Inductively Coupled Plasma Mass Spectrometry was used to analyze the concentration of 13 minerals (Na, K, Ca, Mg, Mn, Zn, Cu, Cr, Sr, Se, Rb, Fe, P) in early (n=54) and later (n=49) breast milk samples collected from Mam ‐Mayan women from rural villages in the Western Highlands of Guatemala. Results : Median concentrations of Na, K, Mn, Zn, Cu, Cr, Rb and Fe were significantly higher in early milk compared with later milk whereas Mg was lower in early milk. No differences were observed between lactation period for Ca, Se and Sr. Within the first 45d, Na, K, Cu and Zn were negatively correlated with infant age. During the 4‐6mo interval, only Se was negatively correlated with infant age. Conclusion : Our findings reveal that concentrations of Na, K, Cu and Zn decreased within the first 45d, that Mn, Cr, Rb and Fe had declined by 4‐6mo, and that Se declined during the 4‐6mo interval. Grant Funding Source : McGill University International Mobility Award, NSERC
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".