Opportunities for sexual transmission of antiretroviral drug resistance among HIV-infected patients in care
Bibliographic record
Abstract
OBJECTIVE: To assess opportunities for transmitted drug resistance (TDR), we examined sexual risk behaviours, HIV viraemia and antiretroviral resistance among patients in care. DESIGN: A retrospective, cross-sectional analysis of clinical cohort data. METHODS: For 244 UNC Center for AIDS Research HIV Clinical Cohort participants, demographic and behavioural data were obtained during in-person interviews between 2000 and 2011. Genotypic resistance tests were interpreted using WHO surveillance drug resistance mutations (SDRMs). Log-linear binomial regression was used to evaluate associations with TDR risk, defined as unprotected sex in the prior 6 months, HIV RNA at least 400 copies/ml and at least one SDRM. RESULTS: Participants included 91 (37%) women and 153 men, of whom 92 (60%) were MSM. Median age was 43 years; 70% were Black (n = 171). Most (97%) were antiretroviral-experienced; 44% had exposure to more than four regimens. Among 204 individuals on antiretrovirals, 42% reported suboptimal adherence and 29% were viraemic. Over half of participants had at least one SDRM (n = 131); 26 (11%) had triple-class resistance. Overall, 70% were sexually active, and 55% used condoms inconsistently. Thirty (12%) reported unprotected sex during periods of drug-resistant viraemia. Higher TDR risk was associated with prior homelessness [adjusted prevalence ratio (aPR) 2.20, 95% confidence interval (CI) 1.16-4.18], active substance use (aPR 3.12, 95% CI 1.47-6.62) and nonsignificantly with MSM (aPR 1.75, 95% CI 0.93-3.28). CONCLUSION: A small but significant proportion of clinic patients with drug-resistant HIV engage in sexual behaviours that place others at risk for TDR. Targeted efforts in secondary prevention could have an impact on TDR incidence, over time.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".