Prevalence and correlates of abscesses among a cohort of injection drug users
Bibliographic record
Abstract
Recent studies have indicated that injection-related infections such as abscesses and cellulitis account for the majority of emergency room visits and acute hospitalizations accrued by local injection drug users. The objective of this analysis was to examine the prevalence and correlates of developing an abscess among a cohort of injection drug users in Vancouver and to identify socio-demographic and drug use variables associated with abscesses at baseline. We examined abscesses among participants enrolled in a prospective cohort of injection drug users. Categorical variables were analyzed using the Pearson's chi-square test and continuous variables were analyzed using the Wilcoxon signed rank test. Among 1 585 baseline participants, 341 (21.5%) reported having an abscess in the last six months. In a logistic regression model that adjusted for all variables that were associated with having an abscess at p < 0.1 in univariate analyses, female gender [odds ratio (OR) = 1.7, [95%CI: 1.2 – 2.4]; p = 0.002), recent incarceration (OR = 1.7, [95%CI: 1.3 – 2.2]; p < 0.001), sex trade involvement (OR = 1.4 [95% CI: 1.0 – 2.0]; p = 0.03), frequent cocaine use (OR = 1.5 [95%CI: 1.2 – 2.0]; p = 0.002) and HIV serostatus (OR = 1.5, [95%CI: 1.2 – 2.0]; p = 0.003) were positively associated with having an abscess. Explanations for these associations require further study, and interventions are needed to address this highly prevalent concern.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".