Translating Clinical Pharmacology Data in Pregnancy to Evidence‐Based Guideline Recommendations: Perspectives From the HIV Field
Bibliographic record
Abstract
Pharmacokinetic (PK) studies in pregnant, postpartum, and breastfeeding people are critical to informing appropriate medication use and dosing. A key component of translating PK results in these complex populations into clinical practice involves the systematic review and interpretation of data by guideline panels, composed of clinicians, scientists, and community members, to leverage available data for informed decision making by clinicians and patients and offer clinical best practices. Interpretation of PK data in pregnancy involves evaluation of multiple factors such as the study design, target population, and type of sampling performed. Assessments of fetal and infant drug exposure while in utero or during breastfeeding, respectively, are also critical for informing whether medications are safe to use during pregnancy and throughout postpartum in lactating people. This review will provide an overview of this translational process, discussion of the various factors considered by guideline panels, and practical aspects of implementing certain recommendations, using the HIV field as an example.
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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 | 0.086 | 0.221 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".