Risk of Mother-to-Child Transmission of HIV among Women on Triple Antiretroviral Drugs in Sub-Saharan Africa: Systematic Review of Observational Studies and Explorative Meta-Analysis.
Bibliographic record
Abstract
INTRODUCTION: Monitoring the elimination of mother-to-child HIV transmission (MTCT) requires measuring the transmission rate; however, in the era of triple antiretroviral drugs (ARVs) for prevention of MTCT, evaluators use different methodological approaches to measure it. This work revised programme evaluation studies between 2000–13 to determine the effect of triple ARVs on MTCT rate in Sub-Sahara Africa (SSA) METHODS: A systematic review and explorative meta-analysis was undertaken. PUBMED and EMBASE were searched using relevant terms. Inclusion criteria were as followed: population, HIV-exposed infants and their mothers; intervention, triple ARVs treatment/prophylaxis; outcome, MTCT rate (proportion); and study design, observational studies. The New-Castle Ottawa scale was used to assess the methodological quality. Random-effect modelling was used to combine individual MTCT rates. Forest plots were used to summarize the results, and funnel plot was used to assess publication bias RESULTS: Thirty studies were reviewed. Most were facility-based or child-testing data of intermediate quality. They were relatively similar in terms of exposure, but the outcome, sample size (10–5141), and type/number of covariates reported varied considerably. Competing risk such as weaning and bias due to loss to follow-up were not usually considered in individual studies, leading to an incorrect estimation of transmission rate. Twelve studies were included in the meta-analysis leading to a pooled estimate of 4% (95% CI 1.6-7.3) with large variations in individual MTCT rates (0.9-11.9%); heterogeneity was high (Q-statistic P < 0.000; I 2 -index 95% CI 92.9–96.5), and the funnel plot was asymmetric. Subgroup analyses by infants' age at testing showed no differences, but the prophylaxis group had a slightly lower pooled estimate than the treatment group (3.7 vs 4.9%)
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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 | 0.024 | 0.063 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.032 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".