HIV and HCV Prevalence and Gender-Specific Risk Profiles of Crack Cocaine Smokers and Dual Users of Injection Drugs
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Bibliographic record
Abstract
The present analysis compares HIV and HCV prevalence and associated gender-specific risk patterns of dual users (i.e., crack smokers who inject drugs) and never injectors. Two logistic models, one restricted to female and the other to male crack smokers, were constructed to identify gender-specific risk factors associated with dual use (p < 0.05). Of 437 crack smokers, 246 (56%) were dual users while 191 (44%) were never injectors. In a fitted logistic regression model, dual use among female crack smokers was associated with HCV infection (adjusted OR = 4.65, 95% CI: 1.92-9.70), exchanging sex for money, drugs, or shelter while using crack (aOR = 4.47, 95% CI: 1.56-12.80), having a casual partner who injects (aOR = 4.13, 95% CI: 1.05-16.26), having equipment broken or confiscated by police without being arrested (aOR = 3.66, 95% CI: 1.43-9.34), and HIV infection (aOR = 2.07, 95% CI: 1.18-5.96). Among male crack smokers, dual use was associated with HCV infection (aOR = 5.34, 95% CI: 2.10-13.18), exchanging sex for money, drugs, or shelter (aOR = 3.25, 95% CI: 1.59-6.65), crack use history >or= 5 years (aOR = 2.16, 95% CI: 1.29-3.63), and smoking in a group of unknown people (such as crack houses, alleys; aOR = 1.70, 95% CI: 1.10-2.81). These findings highlight the need for evidence-based prevention and harm reduction initiatives that directly targeting crack cocaine smokers, with particular attention given to female dual users of injection drugs.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it