Declines in highly active antiretroviral therapy initiation at <scp>CD4</scp> cell counts ≤ 200 cells/μL and the contribution of diagnosis of <scp>HIV</scp> at <scp>CD4</scp> cell counts ≤ 200 cells/μL in <scp>B</scp>ritish <scp>C</scp>olumbia, <scp>C</scp>anada
Bibliographic record
Abstract
OBJECTIVES: The aim of the study was to examine trends in initiating highly active antiretroviral therapy (HAART) with a CD4 count ≤ 200 cells/μL and the contribution of having a CD4 count ≤ 200 cells/μL at the time of diagnosis to these trends, in British Columbia (BC), Canada. METHODS: We included in the analysis treatment-naïve BC residents aged ≥ 19 years who initiated HAART from 2003 to 2012. Participants were classified as follows: Group 1: diagnosed and initiated HAART with a CD4 count > 200 cells/μL; Group 2: diagnosed with a CD4 count > 200 cells/μL and initiated HAART with a CD4 count ≤ 200 cells/μL; and Group 3: diagnosed and initiated HAART with a CD4 count ≤ 200 cells/μL. We measured trends in initiating HAART with a CD4 count ≤ 200 cells/μL and used logistic regression models to measure factors associated with initiating HAART with a CD4 count ≤ 200 cells/μL, stratified by having a CD4 count ≤ 200 cells/μL or > 200 cells/μL at the time of diagnosis. RESULTS: Between 2003 and 2012, 3506 BC residents initiated HAART. Of these, 44% (1558 of 3506) initiated HAART with a CD4 count ≤ 200 cells/μL. This proportion declined from 69% (198 of 287) in 2003 to 21% (81 of 330) in 2012 (P < 0.001). The proportion of those in Group 3 increased from 49% (97 of 198) in 2003 to 69% (56 of 81) in 2012 (P < 0.001). Overall, 56% (1948), 22% (776) and 22% (782) made up Groups 1, 2, and 3, respectively. In adjusted analyses, seeing a specialist was significantly associated with being in Group 3. Using injection drugs and seeing a specialist were associated with being in Group 2. CONCLUSIONS: In recent years, among individuals who ever initiated HAART in BC, being diagnosed with low CD4 cell counts has become a greater contributor to initiating HAART with low CD4 cell counts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".