Comparison of guidelines for HIV viral load monitoring among pregnant and breastfeeding women in sub-Saharan Africa
Bibliographic record
Abstract
BACKGROUND: Intensified viral load monitoring for pregnant and breastfeeding women has been proposed to help address concerns around antiretroviral therapy (ART) adherence, viraemia and transmission risk, but there have been no systematic evaluations of existing policies. METHODS: We used an individual Monte Carlo simulation to describe longitudinal ART adherence and viral load from conception until 2 years' postpartum. We applied national and international guidelines for viral load monitoring to the simulated data. We compared guidelines on the percentage of women receiving viral load monitoring and the percentage of women monitored at the time of elevated viral load. RESULTS: Coverage of viral load monitoring in pregnancy and breastfeeding varied markedly, with between 14% and 100% of women monitored antenatally and 38-98% monitored during breastfeeding. Specific recommendations for testing at either a fixed gestation or a short, fixed period after ART initiation achieved more than 95% testing in pregnancy but this was much lower (14-83%) among guidelines with no special stipulations. By the end of breastfeeding, only a small proportion of simulated episodes of elevated viral load more than 1000 copies/ml were successfully detected by monitoring (range, 20-50%). DISCUSSION: Although further research is needed to understand optimal viral load frequency and timing in this population, these results suggest that current policies yield suboptimal detection of elevated viral load in pregnant and breastfeeding 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 | 0.015 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".