Management of People Who Inject Drugs With Serious Injection-Related Infections in an Outpatient Setting: A Scoping Review
Bibliographic record
Abstract
Background: People who inject drugs (PWID) are at risk of severe injection-related infection (SIRI), which is challenging to manage. We conducted a scoping review to map the existing evidence on management of PWID with SIRI in an outpatient setting. Methods: We conducted a literature search in MEDLINE, Embase, Cochrane Central, and CINAHL from their inception until 6 December 2023. Studies were included if they focused on PWID with SIRI requiring ≥2 weeks of antibiotic therapy, with a proportion of management occurring outside hospitals. Studies were categorized inductively and described. Results: The review included 68 articles with the following themes. PWID generally prefer outpatient management if deemed safe and effective. Most studies support outpatient management, finding it to be as effective and safe as inpatient care, as well as less costly. Successful transition to outpatient management requires multidisciplinary discharge planning with careful consideration of patient-specific factors. Emerging evidence supports the effectiveness and safety of outpatient parenteral antibiotic therapy, long-acting lipoglycopeptides, and oral antibiotic therapy, each having unique advantages and disadvantages. Various specialized outpatient settings, such as skilled nursing facilities and residential treatment centers, are available for management of these infections. Finally, all patients are likely to benefit from adjunctive addiction care. Conclusions: Emerging evidence indicates that outpatient management is effective and safe for SIRI, which is preferred by most PWID. Key components of outpatient management include multidisciplinary discharge planning, appropriate antibiotic modality, suitable care settings, and adjunctive addiction care. These elements should be carefully tailored to patient needs and circumstances.
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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 | 0.006 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| 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".