Maternal HIV status is associated with early breastfeeding practices of Ghanaian infants: Preliminary results from the RIING study
Bibliographic record
Abstract
Exclusive breastfeeding (EBF) is a key behavior to improve child health and reduce the risk of HIV transmission. Data on daily infant feeding practices were available from birth‐6 mo for 218 Ghanaian infants participating in an on‐going cohort study (Research to Improve Infant Nutrition and Growth, RIING) whose mothers were HIV‐infected (HIVI, n=63), uninfected (HIVN, n=82) or not tested (NT, n=73). Total months of EBF did not vary by HIV status; 46% EBF for 6 mo. However, breastfeeding patterns varied from month‐to‐month. At 1 mo, the majority of mothers exclusively breastfed (94%); however, HIVI mothers had lower rates of EBF compared to the other participants (87.3% vs. 96.7%; P<0.05). Compared to other mothers, HIVI women were less likely to continue to EBF from 1–2 mo (81% vs. 92%; P<0.05) but if they were PBF between 1 and 3 mo, they were more likely to change their feeding patterns and start EBF (P<0.05). More (16%) of HIVI mothers changed from EBF to PBF between 3–4 mo compared to only 6% of other mothers (P<0.05). These preliminary results highlight the dynamic nature of breastfeeding among study mothers. Appropriate counseling throughout the first six months postpartum can help mothers exclusively breastfeed to secure their infant's health. Funded by NIH grant HD43620.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".