The Impact of Sex Partners' HIV Status on HIV Seroconversion in a Prospective Cohort of Injection Drug Users
Bibliographic record
Abstract
OBJECTIVE AND DESIGN: The identification of individuals at highest risk of HIV infection is critical for targeting prevention strategies. We evaluated the HIV status of the sex partners of injection drug users (IDUs) and rates of subsequent HIV seroconversion among a prospective cohort study of IDUs. METHODS: We performed an analysis of the time to HIV infection among baseline HIV-negative IDUs enrolled in the Vancouver Injection Drug Users Study. IDUs were stratified based on whether or not they reported having an HIV-positive sex partner. Kaplan-Meier methods were used to estimate cumulative HIV incidence rates, and Cox regression was used to determine adjusted relative hazards (RHs) for HIV seroconversion. RESULTS: Of 1013 initially HIV-negative IDUs, 4.8% had an HIV-positive partner at baseline. After 18 months, the cumulative HIV incidence rate was significantly elevated among those who reported having an HIV-positive sex partner (23.4% vs. 8.1%; log-rank P < 0.001). In a Cox regression model adjusting for all variables that were associated with the time to HIV infection in univariate analyses, including drug use characteristics, having an HIV-positive sex partner (RH = 2.42 [95% confidence interval: 1.30 to 4.60]; P = 0.005) remained independently associated with time to HIV seroconversion. CONCLUSIONS: Having an HIV-positive sex partner was strongly and independently associated with seroconversion after adjustment for risk factors related to drug use. Our findings may aid public health workers in their efforts to identify IDUs who should be targeted with education and prevention efforts and indicate the need for ongoing development of prevention interventions for IDU sex partners who are HIV discordant.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".