Time Trends for HIV-1 Antiretroviral Resistance Among Antiretroviral-Experienced and Naive Pregnant Women in New York City During 1991 to Early 2001
Bibliographic record
Abstract
Time trends in the prevalence of drug resistance to antiretroviral therapy (ART) in pregnant women have not been studied. Treatment and prophylactic efficacy could be compromised by drug-resistant HIV strains. We conducted a repeated cross-sectional study of antiretroviral resistance mutations to nucleoside reverse transcriptase inhibitors (NRTIs) and nonnucleoside reverse transcriptase inhibitors (NNRTIs) and of major mutations to protease inhibitors (PIs) in virus isolates from 300 HIV-infected pregnant women in New York City from 1991 to early 2001. The overall prevalence of mutations for NRTIs from 1991 to early 2001 was higher for ART-experienced (25.6% [95% confidence interval (CI): 19.1% to 32.1%]) than ART-naive (8.6% [95% CI: 3.7% to 13.4%]) mothers (P < 0.002). For NNRTIs, the overall prevalence of mutations was somewhat higher among ART-experienced (5.8% [95% CI: 2.3% to 9.3%]) versus ART-naive (1.6% [95% CI: 0% to 3.7%]) women (P = 0.06), and increased over time for ART-naive women (0%-7.4%; P = 0.03) and ART-experienced women (0%-19.4%; P = 0.0002). The prevalence of PI-associated mutations was also higher overall among ART-experienced mothers (5.8% [95% CI: 2.3% to 9.3%] vs. 1.6% [95% CI: 0% to 3.7%]; P = 0.06), with increases over time seen for ART-naive women (0%-7.4%; P = 0.03) and ART-experienced women (0%-16.1%; P = 0.0008). The increasing prevalence of drug resistance in pregnant women, including those who are drug-naive, underscores the necessity for resistance testing to guide treatment to achieve suppression of the mother's virus.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".