Prevalence and correlates of jugular injections among injection drug users
Bibliographic record
Abstract
BACKGROUND: Jugular injection of drugs has been reported, although little is known about the prevalence of and risk factors associated with this behaviour. We evaluated factors associated with jugular injection among a cohort of injection drug users (IDU) in Vancouver, Canada. METHODS: We used univariate statistics and logistic regression to examine factors associated with jugular injection among participants in the Vancouver Injecting Drug Users Study (VIDUS), a large prospective cohort study of IDU recruited through snowball sampling methods in Vancouver, Canada. FINDINGS: Between December 2004 and November 2005, 780 IDU were followed up as part of VIDUS and 198 (25%) reported jugular injection in the previous 6 months. In multivariate analyses, factors associated independently with jugular injection included: being of the female gender [adjusted odds ratio (aOR) = 1.72, 95% confidence interval (CI): 1.14-2.59; p = 0.010], daily heroin use (aOR = 2.89, 95% CI: 1.93-4.34; p < 0.001), daily cocaine use (aOR = 1.76, 95% CI: 1.12-2.76; p = 0.014], requiring help injecting (aOR = 4.44, 95% CI: 2.64-7.46; p < 0.001), and involvement in the sex-trade (aOR = 2.71, 95% CI: 1.6-4.55; p < 0.001). INTERPRETATION: Reporting a history of jugular injecting was alarmingly high in the cohort and was associated with several identifiable demographic and drug-using characteristics. Given previous reports demonstrating the risk of infection and vascular trauma due to this behaviour, these populations should be considered seriously as a target for safer injecting education.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".