Determinants of Cutaneous Injection-Related Infections Among Injection Drug Users at an Emergency Department
Bibliographic record
Abstract
INTRODUCTION: Cutaneous injection-related infections (CIRI) are a primary reason injection drug users (IDU) access the emergency department (ED). METHODOLOGY: Using Cox proportional hazard regression, we examined predictors of ED use for CIRI, stratified by sex, among 1083 supervised injection facility (SIF) users. RESULTS: Over a four-year period, 289 (27%) visited the ED for CIRI, yielding an incidence density for females of 23.8 (95% confidence interval (CI): 19.3 - 29.0) and males of 19.2 per 100 person-years (95% CI: 16.7 - 22.1). Factors associated with ED use for CIRI among females included residing in the Downtown Eastside (DTES) (adjusted hazard ratio [AHR] = 2.06 [1.13 - 3.78]) and being referred to hospital by SIF nurses (AHR = 4.48 [2.76 - 7.30]). Among males, requiring assistance with injection (AHR = 1.38 [1.01 - 1.90]), being HIV-positive (AHR = 1.85 [1.34 - 2.55]), and being referred to hospital by SIF nurses (AHR = 2.97 [1.93 - 4.57]) were associated with an increased likelihood of an ED visit for CIRI. CONCLUSION: These results suggest SIF nurses have facilitated referral of hospital treatment for CIRI, highlighting the need for continued development of efficient and collaborative efforts to reduce the burden of CIRI.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.004 | 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".