Physician experience and rates of plasma HIV-1 RNA suppression among illicit drug users: an observational study
Bibliographic record
Abstract
BACKGROUND: Despite the availability of antiretroviral therapy (ART), suboptimal treatment outcomes have been observed among HIV-seropositive illicit drug users. As there is an urgent need to improve responses to antiretroviral therapy among this population, we undertook this study to evaluate the role of physician experience on rates of plasma HIV-1 RNA suppression following initiation of ART. METHODS: Using data from a community-recruited cohort of HIV-positive illicit drug users, we used Cox proportional hazards regression to model the time to plasma viral HIV RNA < 500 copies/mL among antiretroviral-naïve subjects initiating ART. Physician experience was defined as a continuous variable measured per 100 HIV-infected patients previously enrolled in the province-wide HIV treatment registry by that physician at the time a patient was enrolled. RESULTS: Between May 1996 and December 2008, 267 individuals initiated ART among whom 227 (85%) achieved a plasma HIV RNA < 500 copies/mL during the study period. In a multivariate analysis, greater physician experience was independently associated with higher rates of plasma HIV RNA suppression (adjusted hazard ratio [AHR] = 1.17, 95% confidence interval [CI]: 1.03-1.34) after adjustment for adherence to ART. Other factors associated with viral suppression included engagement in methadone maintenance therapy (AHR = 1.61, 95% CI: 1.23-2.09), ≥ 95% adherence to ART (AHR = 2.42, 95% CI: 1.80-3.26), baseline CD4 count (AHR = 0.89, 95% CI: 0.83-0.96) and baseline plasma HIV-1 RNA (AHR = 0.65, 95% CI: 0.53-0.81). CONCLUSIONS: In this setting of universal HIV/AIDS care, illicit drug users with more experienced physicians exhibited faster rates of plasma viral load suppression. These findings argue for specialized services to help optimize HIV treatment outcomes among this 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 | 0.005 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".