Increases in CD4 T-cell count at antiretroviral therapy initiation among HIV-positive illicit drug users during a treatment-as-prevention initiative in Canada
Bibliographic record
Abstract
Background: Although treatment-as-prevention (TasP) efforts are a new cornerstone of efforts to respond to the HIV/AIDS pandemic, their effects among people who use drugs (PWUD) have not been fully evaluated. This study characterizes temporal trends in CD4+ T-cell (CD4) count at ART initiation and rates of virological response among HIV-positive PWUD during a TasP initiative. Methods: We used data on individuals initiating ART within a prospective cohort of PWUD linked to comprehensive clinical records. Using multivariable linear regression, we evaluated the relationship between CD4 count prior to ART initiation and year of initiation and time to HIV-1 RNA viral load <50 copies/ml following initiation using Cox proportional hazards modelling. Results: Among 355 individuals, CD4 count at initiation rose from 130 to 330 cells/ml from 2005 to 2013. In multivariable regression, initiation year was significantly associated with higher CD4 count (β=29.5 cells per year, 95% CI 21.0, 37.9). Initiating ART at higher CD4 counts was significantly associated with optimal viral response (adjusted hazard ratio =1.13 per 100 cells/ml increase, 95% CI 1.05, 1.22). Conclusions: Increases in CD4 cell count at initiation over time was associated with superior virological response, consistent with the aims of the TasP initiative.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".