Age-specific and sex-specific weight gain norms to monitor antiretroviral therapy in children in low-income and middle-income countries
Bibliographic record
Abstract
BACKGROUND: Viral load and CD4% are often not available in resource-limited settings for monitoring children's responses to antiretroviral therapy (ART). We aimed to construct normative curves for weight gain at 6, 12, 18, and 24 months following initiation of ART in children, and to assess the association between poor weight gain and subsequent responses to ART. DESIGN: Analysis of data from HIV-infected children younger than 10 years old from African and Asian clinics participating in the International epidemiologic Databases to Evaluate AIDS. METHODS: The generalized additive model for location, scale, and shape was used to construct normative percentile curves for weight gain at 6, 12, 18, and 24 months following ART initiation. Cox proportional models were used to assess the association between lower percentiles (< 50th) of weight gain distribution at the different time points and subsequent death, virological suppression, and virological failure. RESULTS: Among 7173 children from five regions of the world, 45% were underweight at baseline. Weight gain below the 50th percentile at 6, 12, 18, and 24 months of ART was associated with increased risk of death, independent of baseline characteristics. Poor weight gain was not associated with increased hazards of virological suppression or virological failure. CONCLUSION: Monitoring weight gain on ART using age-specific and sex-specific normative curves specifically developed for HIV-infected children on ART is a simple, rapid, sustainable tool that can aid in the identification of children who are at increased risk of death in the first year of ART.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".