Baseline self-perceived risk of HIV infection independently predicts the rate of HIV seroconversion in a prospective cohort of injection drug users
Bibliographic record
Abstract
BACKGROUND: The identification of individuals at the highest risk of human immunodeficiency virus (HIV) infection is critical for targeting prevention strategies. We evaluated self-perceived risk of HIV infection and rates of subsequent HIV seroconversion among a prospective cohort study of injection drug users (IDUs). METHODS: We performed an analysis of the time to HIV infection among 994 baseline HIV negative IDUs enrolled in the Vancouver injection drug users study (VIDUS). IDUs were stratified based on their baseline self-perceived risk of HIV seroconversion (higher than others vs same or lower). Kaplan-Meier methods were used to estimate cumulative HIV incidence rates and Cox regression was used to determine adjusted relative hazards for HIV seroconversion. RESULTS: At the end of 24 months after enrolment into the cohort, the cumulative HIV incidence rate was significantly elevated among the 5.9% of the sample who perceived their risk for HIV infection to be higher at baseline (26.6% vs 7.8% log-rank P < 0.001). In a Cox model that adjusted for all variables that were associated with the time to HIV infection in univariate analyses, a higher baseline self-perceived risk of acquiring HIV infection (relative hazard RH: 2.48 [95% Confidence interval (CI): 1.51, 4.10]; P = 0.004) remained independently associated with time to HIV seroconversion. CONCLUSIONS: IDUs' perception of their risk for HIV seroconversion upon enrolment into a prospective cohort study was strongly and independently associated with the subsequent rate of HIV seroconversion. Since this risk marker remained independently associated with HIV seroconversion, even after adjustment for time-updated risk behaviours, our findings have major implications that may aid outreach workers in their efforts to identify IDUs who should be targeted with prevention efforts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".