Treatment Experience and Repeat Pregnancy Impact the Effectiveness of Non-Nucleoside Reverse Transcription Inhibitor-Highly Active Antiretroviral Therapy for the Prevention of Mother to Child Transmission of Human Immunodeficiency Virus
Bibliographic record
Abstract
Non-nucleoside reverse transcription inhibitor (NNRTI)-containing antiretroviral therapy (ART) for the prevention of mother to child transmission (PMTCT) of human immunodeficiency virus (HIV) has led to dramatic reductions in perinatal HIV infection in resource-constrained settings. Nonetheless, PMTCT programs are complicated by repeat pregnancies, in which long-term or repeat exposures to PMTCT regimens over time may lead to the acquisition of HIV drug resistance mutations, and consequent treatment failure. In this study, we retrospectively assessed the effectiveness of the NNRTI-based PMTCT protocol from 2008 to 2010 in The Bahamas National HIV/AIDS Program. We show that women who had been in repeat pregnancies and those who were already prescribed ART at conception were at increased risk of virologic failure, relative to treatment-inexperienced women and primigravida, respectively (AOR 3.1, 95% CI: 1.3–7.1, p = .008 and AOR 5.0, 95% CI: 1.8–14.1, p = .002). In addition, women undergoing treatment at conception were more likely to possess HIVDR mutations relative to treatment-naive women (AOR 447.1, 95% CI: 17.9–11,173.5, p = .001). Therefore, individual treatment history is a key metric determining the effectiveness of current and future PMTCT interventions. The implications of this to PMTCT programmatic success in light of the most recent WHO guidelines are discussed.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".