Risk factors for developing a cutaneous injection-related infection among injection drug users: a cohort study
Bibliographic record
Abstract
BACKGROUND: Cutaneous injection-related infections (CIRI), such as abscesses and cellulitis, are common and preventable among injection drug users (IDU). However, risk factors for CIRI have not been well described in the literature. We sought to characterize the risk factors for current CIRI among individuals who use North America's first supervised injection facility (SIF). METHODS: A longitudinal analysis of factors associated with developing a CIRI among participants enrolled in the Scientific Evaluation of Supervised Injecting (SEOSI) cohort between January 1, 2004 and December 31, 2005 was conducted using generalized linear mixed-effects modelling. RESULTS: In total, 1065 participants were eligible for this study. The proportion of participants with a CIRI remained under 10% during the study period. In a multivariate generalized linear mixed-effects model, female sex (Adjusted Odds Ratio (AOR) = 1.68 [95% Confidence Interval (CI): 1.16-2.43]), unstable housing (AOR = 1.49 [95% CI: 1.10-2.03]), borrowing a used syringe (AOR = 1.60 [95% CI: 1.03-2.48]), requiring help injecting (AOR = 1.42 [95% CI: 1.03-1.94]), and injecting cocaine daily (AOR = 1.41 [95% CI: 1.02-1.95]) were associated with an increased risk of having a CIRI. CONCLUSION: CIRI were common among a subset of IDU in this study, including females, those injecting cocaine daily, living in unstable housing, requiring help injecting or borrowing syringes. In order to reduce the burden of morbidity associated with CIRI, targeted interventions that address a range of factors, including social and environmental conditions, are needed.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".