A hierarchical modeling approach for assessing the safety of exposure to complex antiretroviral drug regimens during pregnancy
Bibliographic record
Abstract
Combination antiretroviral regimens have achieved tremendous success in reducing perinatal HIV transmission, and have become standard of care in pregnant women with HIV. However, the large variety of combination antiretroviral regimens utilized in practice raises the question of whether some of these highly potent drugs pose other risks to the pregnancy or infant. While HIV-infected pregnant women are almost always exposed to multiple antiretrovirals concurrently, standard safety screening strategies typically consider each individual antiretroviral separately, which fails to account for potential confounding due to simultaneous exposure to other antiretrovirals. In this paper, we evaluate a hierarchical modeling approach which groups antiretrovirals by drug class to screen for the safety of antiretrovirals taken during pregnancy, while still providing individual antiretroviral drug effect estimates. In simulation studies, we observed that the hierarchical approach may be advantageous as compared to considering each antiretroviral drug separately or simultaneously evaluating all antiretrovirals in a fixed effect model, particularly when there is prior evidence suggesting drugs from the same class behave similarly on the outcome. The characteristics of the hierarchical approach are illustrated in an application evaluating risk of preterm birth using a study including over 2000 pregnancies representing over 100 antiretroviral combinations, each involving up to three drug classes.
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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 | 0.018 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 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".