Temporal trends in the discontinuation of first-line antiretroviral therapy
Bibliographic record
Abstract
OBJECTIVES: The aim of this study was to describe the rates and predictors of discontinuing first-line antiretroviral therapy in the different eras of treatment over a nearly 20 year period initiated in British Columbia between 1992 and 2010. METHODS: All naive adults who started antiretroviral therapy (first-line antiretroviral therapy) at any hospital or clinic in British Columbia (Canada) in 1992-2010 were included in this population-based retrospective cohort study. We were primarily interested in whether the era of treatment (1992-95, 1996-2000, 2001-05 and 2006-10) was associated with discontinuation (stopping or switching of initial treatment) within 3 years of starting therapy. Weibull survival analysis was used to model the era of treatment and its association with time to discontinuation. RESULTS: The study included 7901 patients. Overall, the probability of discontinuing at 12, 24 and 36 months of treatment was 52%, 68% and 76%, respectively. In the adjusted model, variables associated with discontinuing were earlier treatment era, younger age, low adherence and lower baseline CD4 count. Regarding the 2006-10 period, the probability of discontinuing at 12, 24 and 36 months was 36%, 47% and 53%, respectively. In the adjusted model, the variables associated with discontinuation were younger age, female gender, AIDS-defining illnesses at baseline, low adherence and a protease inhibitor (PI)-based regimen. CONCLUSIONS: Discontinuation rates of first-line therapy have decreased over time, but are still quite high even for the latest drug combinations. In the most recent era, younger women on a PI regimen and those not achieving optimal adherence had the highest risk of discontinuing first-line antiretroviral therapy.
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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 | 0.002 |
| 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".