Daily alcohol use as an independent risk factor for HIV seroconversion among people who inject drugs
Bibliographic record
Abstract
AIMS: To estimate the relationship between daily alcohol use and HIV seroconversion among people who inject drugs (PWID) in a Canadian setting. DESIGN AND SETTING: Data from an open prospective cohort study of PWID in Vancouver, Canada, recruited via snowball sampling and street outreach between May 1996 and November 2013. An interviewer-administered questionnaire including standardized behavioural assessment and HIV antibody testing were conducted semi-annually. Baseline HIV-seronegative participants completing ≥ 1 follow-up visits were eligible for the present analysis. PARTICIPANTS: A total of 1683 eligible participants, were followed for a median of 79.8 [interquartile range (IQR) = 33.3-119.1] months. MEASUREMENTS: The primary end-point was time to HIV seroconversion, with the date of HIV seroconversion estimated as the mid-point between the last negative and the first positive antibody test results. The primary explanatory variable was self-reported daily alcohol use in the previous 6 months assessed semiannually. Other covariates considered included demographic, behavioural, social/structural and environmental risk factors for HIV infection among PWID (e.g. daily cocaine injection, methadone use, etc.). FINDINGS: Of 1683 PWID, there were 176 HIV seroconversions during follow-up with an incidence density of 1.5 [95% confidence interval (CI) = 1.3-1.7] cases per 100 person-years. At baseline, 339 (20.1%) consumed alcohol at least daily in the previous 6 months. In multivariable extended Cox regression analyses, daily alcohol use remained associated independently with HIV seroconversion (adjusted hazard ratio: 1.48; 95% CI = 1.00-2.17). CONCLUSIONS: Daily alcohol use appears to be an independent risk factor for HIV seroconversion among our cohort of PWID.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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 | 0.001 | 0.001 |
| 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".