Comparative Effectiveness of Initial Antiretroviral Therapy Regimens
Bibliographic record
Abstract
BACKGROUND: The generalizability of antiretroviral therapy (ART) clinical trial efficacy findings to routine care settings is not well studied. We compared the relative effectiveness of initial ART regimens estimated in AIDS Clinical Trial Group (ACTG) randomized controlled trials with that among patients receiving ART at Antiretroviral Therapy Cohort Collaboration (ART-CC) study sites. METHODS: Treatment-naive HIV-infected patients initiating identical ART regimens in ACTG trials (A5095 and A5142) and at 15 ART-CC cohort study sites were included. Virological failure (HIV-1 RNA >200 copies/mL) at 24 and 48 weeks, incident AIDS-defining events and mortality were measured according to study design (ART-CC cohort vs. ACTG trial) and stratified by third drug [abacavir (ABC), efavirenz (EFV), and lopinavir/r (LPV/r)]. We used logistic regression to estimate and compare odds ratios (OR) for virological failure between different regimens and study designs, and used Cox models to estimate and compare hazard ratios for AIDS and death. RESULTS: Compared with patients receiving ABC, those receiving EFV had roughly half the odds of 24-week virologic failure (>200 copies/mL) in both ACTG 5095 (OR = 0.53, 95% confidence interval: 0.36 to 0.79) and ART-CC (0.46, 0.37 to 0.57). Virologic superiority of EFV (vs. ABC) seemed comparable in ART-CC and ACTG 5095 (ratio of ORs 0.86, 95% confidence interval: 0.54 to 1.35). Odds ratios for 48-week virologic failure, comparing EFV with LPV/r, were also comparable in ACTG 5142 and ART-CC (ratio of ORs: 0.87, 0.45 to 1.69). CONCLUSIONS: Between ART regimen virologic efficacy of third drugs ABC, EFV, and LPV/r observed in the ACTG 5095 and 5142 trials seem generalizable to the routine care setting of ART-CC clinical cohorts.
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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 | 0.047 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".