Time-independent Maternal and Infant Factors and Time-dependent Infant Morbidities including HIV Infection, Contribute to Infant Growth Faltering during the First 2 Years of Life
Why this work is in the frame
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Bibliographic record
Abstract
Studies investigating the predictors of growth in infants born to HIV-infected women in developing countries are limited. Using data from 886 Tanzanian HIV-infected women and their infants, we examined the impact of maternal socioeconomic and immunological status, infant characteristics at birth, and HIV, diarrhea and respiratory infections on infants' monthly length-for-age (LAZ) and length-for-weight (WLZ) z-scores during the first 2 years of life. We used restricted cubic splines to estimate average adjusted growth curves by categories of each predictor. LAZ decreased significantly during the first 2 years. WLZ increased from birth to 4 months but decreased significantly thereafter. Greater maternal schooling significantly reduced deterioration in LAZ and WLZ scores from birth to 24 months, while maternal CD4 cell counts >or=200 mm(-3) at baseline were associated with reduced deterioration in LAZ scores. Infants born pre-term or with low-birth weight were significantly more stunted and wasted than their reference groups at all time points though their rate of growth faltering was slower. Infant-HIV status was strongly associated with significantly greater deterioration in LAZ and WLZ scores, beginning at about 4 months of age. Episodes of diarrhea or respiratory infections were related to significantly lower WLZ but not LAZ scores, independent of infant-HIV status. In conclusion, maternal schooling, immunological status and infant infections are important predictors of early growth in children born to HIV-positive women.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it