Improved adherence to modern antiretroviral therapy among HIV-infected injecting drug users
Bibliographic record
Abstract
Objectives: Adherence to antiretroviral therapy (ART) among injection drug users (IDU) is often sub-optimal, yet little is known about changes in patterns of adherence since the advent of highly active antiretroviral therapy in 1996. We sought to assess levels of optimal adherence to ART among IDU in a setting of free and universal HIV care. Methods: Data was collected through a prospective cohort study of HIV-positive IDU in Vancouver, British Columbia. We calculated the proportion of individuals achieving at least 95% adherence in the year following initiation of ART from 1996 to 2009. Results: Among 682 individuals who initiated ART, the median age was 37 (31–44) years with 248 (36.4%) female participants. The proportion achieving at least 95% adherence increased over time from 19.3% in 1996 to 65.9% in 2009 (Cochrane-Armitage test for trend: p < 0.001). In a logistic regression model examining factors associated with 95% adherence, initiation year was statistically significant (Odds Ratio = 1.08, 95% Confidence Interval: 1.03–1.13, p < 0.001 per year after 1996) after adjustment for a range of drug use variables and other potential confounders. Conclusions: The proportion of IDU achieving at least 95% adherence during the first year of ART has consistently increased over a 13-year period. Although improved tolerability and convenience of modern ART regimens likely explain these positive trends, by the end of the study period a substantial proportion of IDU still had sub-optimal adherence demonstrating the need for additional adherence support strategies.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".