Optimizing the World Health Organization algorithm for HIV vertical transmission risk assessment by adding maternal self-reported antiretroviral therapy adherence
Bibliographic record
Abstract
BACKGROUND: The World Health Organization (WHO) risk assessment algorithm for vertical transmission of HIV (VT) assumes the availability of maternal viral load (VL) result at delivery and early viral control 4 weeks after initiating antiretroviral treatment (ART). However, in many low-and-middle-income countries, VL is often unavailable and mothers' ART adherence may be suboptimal. We evaluate the inclusion of the mothers' self-reported adherence into the established WHO-algorithm to identify infants eligible for enhanced post-natal prophylaxis when mothers' VL result is not available at delivery. METHODS: We used data from infants with perinatal HIV infection and their mothers enrolled from May-2018 to May-2020 in Mozambique, South Africa, and Mali. We retrospectively compared the performance of the WHO-algorithm with a modified algorithm which included mothers' adherence as an additional factor. Infants were considered at high risk if born from mothers without a VL result in the 4 weeks before delivery and with adherence <90%. RESULTS: At delivery, 143/184(78%) women with HIV knew their status and were on ART. Only 17(12%) obtained a VL result within 4 weeks before delivery, and 13/17(76%) of them had VL ≥1000 copies/ml. From 126 women on ART without a recent VL result, 99(79%) had been on ART for over 4 weeks. 45/99(45%) women reported suboptimal (< 90%) adherence. A total of 81/184(44%) infants were classified as high risk of VT as per the WHO-algorithm. The modified algorithm including self-adherence disclosure identified 126/184(68%) high risk infants. CONCLUSIONS: In the absence of a VL result, mothers' self-reported adherence at delivery increases the number of identified infants eligible to receive enhanced post-natal prophylaxis.
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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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| 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".