Regimen‐dependent variations in adherence to therapy and virological suppression in patients initiating protease inhibitor‐based highly active antiretroviral therapy
Bibliographic record
Abstract
OBJECTIVE: To examine differences among four protease inhibitor (PI)-based drug regimens in adherence to therapy and rate of achievement of virological suppression in a cohort of antiretroviral-naive patients initiating highly active antiretroviral therapy (HAART). METHODS: Participants were antiretroviral-naive and were first dispensed combination therapy containing two nucleosides and a ritonavir (RTV)-boosted PI, or unboosted nelfinavir, between 1 January 2000 and 30 September 2003. Logistic regression analysis was used to examine associations between the prescribed PI and other baseline factors associated with being >90% adherent to therapy and then to determine the associations of prescribed drug regimen, adherence to therapy and baseline variables with the odds of achieving two consecutive viral loads of <500 HIV-1 RNA copies/mL. RESULTS A total of 385 subjects were available for analysis. Lopinavir (LPV)/RTV was prescribed for 168 patients (42% of total); 86 (22%) received indinavir (IDV)/RTV; 91 (24%) received nelfinavir (NFV) and 40 (10%) received saquinavir (SQV)/RTV. SQV/RTV-based HAART was associated with reduced adherence to therapy [odds ratio (OR)=0.40; 95% confidence interval (CI) 0.19-0.83]. In multivariate models, IDV/RTV (OR=0.45; 95% CI 0.22-0.92), SQV/RTV (OR=0.18; 95% CI 0.07-0.43) and NFV were associated with reduced odds of achieving virological suppression within 1 year in comparison to LPV/RTV-based therapy. For patients receiving NFV, adjusting for adherence (OR=0.73; 95% CI 0.36-1.47) rendered this association nonsignificant. CONCLUSION: Patients prescribed IDV/RTV, NFV or SQV/RTV were less likely to achieve virological suppression on their first regimen compared with patients prescribed LPV/RTV. Reduced adherence to these therapies only partly explained these observed differences.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".