Use of Newer Antiretroviral Agents, Darunavir and Etravirine with or without Raltegravir, in Pregnancy: A Report of two Cases
Bibliographic record
Abstract
BACKGROUND: Although antiretroviral therapy during pregnancy is associated with significant reductions in the risk of vertical transmission of HIV, attainment of this outcome in highly treatment-experienced pregnant women might be complicated by the lack of active drugs available to assemble a potent regimen. The recent licensing and availability of darunavir, etravirine and raltegravir has broadened management options available for highly treatment-experienced patients. However, data on their safety and efficacy in preventing vertical transmission are limited. METHODS: A retrospective chart review of two cases describing obstetrical, infant and treatment outcomes associated with the use of regimens that include darunavir and etravirine with or without raltegravir during pregnancy was conducted. RESULTS: We document two cases of pregnant HIV-positive women treated with antiretroviral therapy including darunavir, etravirine and raltegravir. Vertical transmission was averted and no congenital anomalies were observed. CONCLUSIONS: In the absence of human development toxicity data for these agents, these cases provide preliminary anecdotal data on their safety during pregnancy. Although the outcomes of these cases are reassuring, additional studies and registries are required to establish the safety and efficacy of these agents during pregnancy.
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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) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".