<i>In Utero</i> and Postnatal Exposure to Antiretrovirals Among HIV-Exposed But Uninfected Children in the United States
Bibliographic record
Abstract
An increasing number of antiretroviral agents (ARVs) are approved for use, but their use during pregnancy in the United States has not been completely described. We used data from the Pediatric HIV/AIDS Cohort Study (PHACS) Surveillance Monitoring for ART Toxicities (SMARTT) study, a United States-based prospective cohort study of HIV-exposed but uninfected children, to assess temporal trends and maternal characteristics associated with the use of ARVs during pregnancy. The proportion of children exposed in utero to ARVs was calculated over time. A multivariable logistic regression model was used to estimate associations of maternal characteristics with use of highly active antiretroviral therapy (HAART) during pregnancy. We studied 1768 HIV-exposed but uninfected children born between 1995 and 2009 and enrolled in SMARTT. Prenatal HAART exposure increased from 19% in 1997 to 88% in 2009. Of children born in 2009, 99% had prenatal exposure to NRTIs (including zidovudine, 73%; lamivudine, 72%; tenofovir, 39%; and emtricitabine, 37%). Exposure to protease inhibitors increased from 15% in 1997 to 86% in 2009, while exposure to non-nucleoside reverse transcriptase inhibitors (NNRTIs) declined from 33% in 2003 to 11% in 2009. Higher maternal HIV RNA viral load (VL) concentration, lower maternal CD4 count, and earlier timing of the first maternal CD4 or VL measurement during pregnancy were associated with increased odds of HAART exposure. Prenatal HAART exposure has increased but is not universal. As ARV use during pregnancy continues to evolve, follow-up of children is needed to assess long-term effects of ARV exposures.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".