Maternal <scp>HIV</scp> is associated with reduced growth in the first year of life among infants in the <scp>E</scp>astern region of <scp>G</scp>hana: the <scp>R</scp>esearch to <scp>I</scp>mprove <scp>I</scp>nfant <scp>N</scp>utrition and <scp>G</scp>rowth (<scp>RIING</scp>) <scp>P</scp>roject
Bibliographic record
Abstract
Children of HIV-infected mothers experience poor growth, but not much is understood about the extent to which such children are affected. The Research to Improve Infant Nutrition and Growth (RIING) Project used a longitudinal study design to investigate the association between maternal HIV status and growth among Ghanaian infants in the first year of life. Pregnant women in their third trimester were enrolled into three groups: HIV-negative (HIV-N, n = 185), HIV-positive (HIV-P, n = 190) and HIV-unknown (HIV-U, n = 177). Socioeconomic data were collected. Infant weight and length were measured at birth and every month until 12 months of age. Weight-for-age (WAZ), weight-for-length (WLZ) and length-for-age (LAZ) z-scores were compared using analysis of covariance. Infant HIV status was not known as most mothers declined to test their children's status at 12 months. Adjusted mean WAZ and LAZ at birth were significantly higher for infants of HIV-N compared with infants of HIV-P mothers. The prevalence of underweight at 12 months in the HIV-N, HIV-P and HIV-U were 6.6%, 27.5% and 9.9% (P < 0.05), respectively. By 12 months, the prevalence of stunting was significantly different (HIV-N = 6.0%, HIV-P = 26.5% and HIV-U = 5.0%, P < 0.05). The adjusted mean ± SE LAZ (0.57 ± 0.11 vs. -0.95 ± 0.12; P < 0.005) was significantly greater for infants of HIV-N mothers than infants of HIV-P mothers. Maternal HIV is associated with reduce infant growth in weight and length throughout the first year of life. Children of HIV-P mothers living in socioeconomically deprived communities need special support to mitigate any negative effect on growth performance.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".