Antiretroviral resistance and high-risk transmission behavior among HIV-positive patients in clinical care
Bibliographic record
Abstract
BACKGROUND: HIV-positive patients receiving antiretroviral therapy (ART) who engage in HIV transmission behaviors may harbor and transmit drug-resistant HIV. However, little is known about the risk behaviors of these patients, potential partners exposed and the relationship of these to ART resistance. OBJECTIVE: To determine the relationship of HIV drug resistance and continuing HIV transmission risk behavior among HIV-positive patients in care. METHODS: A retrospective, cross-sectional study of HIV transmission risk behavior and HIV drug resistance data from 333 HIV-positive patients. RESULTS: Among a diverse population of 333 HIV-positive patients, 75 (23%) had unprotected sex during the previous 3-months, resulting in 1126 unprotected sexual events with 191 partners of whom 155 were believed by patients to be HIV-negative or of unknown status. Eighteen of the 75 (24%) had resistant HIV and 207 unprotected sexual events, exposing 18% of the HIV- or status unknown partners. There was no difference in the proportion of patients engaging in unprotected sex who had undetectable viral load (VL) (22%): VL > 400 copies/ml without resistance (20%) and VL > 400 copies/ml with resistance (26%). Resistance and risk behavior was predicted only by lower mental health scores (odds ratio, 10.3; 95% confidence interval, 1.7-18.6). CONCLUSION: A substantial minority (23%) of patients in clinical care engaged in HIV sexual transmission risk behavior. A small subset of these also had ART-resistant HIV. However, this core group (approximately 5% of all patients) accounted for a large number of high-risk HIV transmission events with resistant virus, exposing a substantial number of partners.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".