Smoking of crack cocaine as a risk factor for HIV infection among people who use injection drugs
Bibliographic record
Abstract
BACKGROUND: Little is known about the possible role that smoking crack cocaine has on the incidence of HIV infection. Given the increasing use of crack cocaine, we sought to examine whether use of this illicit drug has become a risk factor for HIV infection. METHODS: We included data from people participating in the Vancouver Injection Drug Users Study who reported injecting illicit drugs at least once in the month before enrolment, lived in the greater Vancouver area, were HIV-negative at enrolment and completed at least 1 follow-up study visit. To determine whether the risk of HIV seroconversion among daily smokers of crack cocaine changed over time, we used Cox proportional hazards regression and divided the study into 3 periods: May 1, 1996-Nov. 30, 1999 (period 1), Dec. 1, 1999-Nov. 30, 2002 (period 2), and Dec. 1, 2002-Dec. 30, 2005 (period 3). RESULTS: Overall, 1048 eligible injection drug users were included in our study. Of these, 137 acquired HIV infection during follow-up. The mean proportion of participants who reported daily smoking of crack cocaine increased from 11.6% in period 1 to 39.7% in period 3. After adjusting for potential confounders, we found that the risk of HIV seroconversion among participants who were daily smokers of crack cocaine increased over time (period 1: hazard ratio [HR] 1.03, 95% confidence interval [CI] 0.57-1.85; period 2: HR 1.68, 95% CI 1.01-2.80; and period 3: HR 2.74, 95% CI 1.06-7.11). INTERPRETATION: Smoking of crack cocaine was found to be an independent risk factor for HIV seroconversion among people who were injection drug users. This finding points to the urgent need for evidence-based public health initiatives targeted at people who smoke crack cocaine.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".