Energy and nutrient intakes from complementary foods are low among infants of HIV‐positive mothers in the Eastern region of Ghana
Bibliographic record
Abstract
Inadequate complementary feeding contributes to poor growth and development of infants and young children in sub‐Saharan Africa. This study assessed the adequacy of energy and nutrients from complementary foods consumed by infants born to HIV‐positive (HIV‐P, n=67), HIV‐negative (HIV‐N, n=85) and HIV‐status unknown (HIV‐U, n=72) mothers. All foods and liquids other than breast milk that were consumed by infants over 24 hours at 6 mo (n=103) and 9 mo (n=121) of age are reported here. Intakes were compared with the WHO estimated requirements from complementary foods assuming an average intake of breast milk. Energy and nutrient intakes from complementary foods significantly increased with infant age. However, with the exception of protein and vitamin A, foods were inadequate in meeting the age‐specific estimated requirements. Compared to children of HIV‐N mothers, children of HIV‐P mothers had significantly lower median intakes of energy (113 kcal vs. 77 kcal), protein (3.7 g vs. 1.8 g), fat (2.1 g vs. 0.6 g), zinc (0.8 mg vs. 0.3 mg), thiamin (0.9 mg vs. 0.2 mg), riboflavin (0.05 mg vs. 0.02 mg) and niacin (0.6 mg vs. 0.2 mg) at 6 mo (p<0.05). There is a need to address sub‐optimal complementary feeding in Ghana, particularly among households affected by HIV. Funded by NIH/NICHD HD 43260.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".