Can Contingency Management Solve the Problem of Adherence to Antiretroviral Therapy in Drug-Dependent Individuals?
Bibliographic record
Abstract
Drug misuse among people living with HIV (human immunodeficiency virus) is associated with higher mortality. It is a frequently observed reason for treatment abandonment, with people who misuse drugs showing a 10 to 25 times higher risk of HIV than the general population. The authors conducted a systematic review and meta-analysis to assess the efficacy of contingency management (CM) to improve adherence to antiretroviral therapy in people living with HIV and substance use disorder (SUD). The inclusion criteria consisted of studies written in English, Italian, Spanish, German, and French; studies conducted with humans; and clinical trials that combined SUD treatment with CM for people living with HIV. Two hundred twenty-two articles were identified, five met all inclusion criteria, and three provided enough data to perform the meta-analysis. We considered treatment adherence by measuring the increase in the CD4 count as our primary outcome. We found a significant increase in treatment adherence in the patient group compared with the control groups during the intervention phase. Positive findings did not persist after the cessation of the incentives. The meta-analysis showed that the intervention improved patient adherence by 2.69 (95% confidence interval: [0.08, 0.51]; p = .007) compared with the control group during the intervention period. All short-term CM studies converged on a positive result for adherence to antiretroviral therapy.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".