Dermatologic Manifestations of Underlying Infectious Disease among Illicit Injection-Drug Users
Bibliographic record
Abstract
BACKGROUND: Drug use patterns and serious bloodborne infections commonly have dermatologic manifestations among illicit injection-drug users (IDUs). OBJECTIVE: To assess how self-reported skin conditions of IDUs may correlate with underlying infectious diseases after adjustment for drug use patterns. METHODS: Prospective analysis of factors associated with self-reports of skin rashes, cellulitis, oral lesions, and lymphadenopathy obtained from 1,065 IDUs enrolled in a large cohort study. Variables potentially associated with each outcome were evaluated using multivariate generalized estimating equations. RESULTS: In multivariate analyses, drug use patterns were associated with cellulitis, whereas human immunodeficiency virus (HIV) infection and hepatitis C (HCV) were not. HCV infection was independently associated with skin rashes (odds ratio [OR] 1.85; 95% CI 1.17-2.94). HIV infection was independently associated with lymphadenopathy (OR 2.00; 95% CI 1.52-2.63), skin rash (OR 2.12; 95% CI 1.57-2.86), and oral lesions (OR 14.95; 95% CI 9.41-23.76). CONCLUSIONS: Self-reports of IDUs, which could easily be obtained as part of a functional inquiry in a clinical setting, correlate with specific drug use patterns and underlying bloodborne infections.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".