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
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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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".