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 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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".