Management of HCV and HIV infections among people who inject drugs
Bibliographic record
Abstract
PURPOSE OF REVIEW: Despite a high burden of hepatitis C virus (HCV) and HIV infection among IDUs and the advent of effective therapies, assessment and treatment remain limited. The current review focuses on the management of HCV and HIV among IDUs, focusing particularly on recent strategies to enhance assessment, uptake and response to HCV and HIV treatment. RECENT FINDINGS: There are compelling data demonstrating that with the appropriate programs, treatment for HIV and HCV among IDUs is successful. However, assessment and treatment for HCV and HIV lags far behind the numbers of IDUs who could benefit from therapy, related to systems, provider and patient-related barriers to care. Strategies for enhancing assessment and treatment for HCV and HIV have been developed, including novel models integrating HCV/HIV care within existing community-based and drug and alcohol clinics, innovative methods for education delivery (including peer-support models) and directly observed therapy. SUMMARY: As we move forward, research must move beyond demonstrating that HCV and HIV infections can be successfully treated among IDUs. There is clear evidence that this is both feasible and effective. Novel strategies to enhance assessment, uptake and response to treatment should be evaluated among IDUs to elucidate mechanisms to enhance care for this underserved population.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".