Comparing the efficacy of efavirenz and boosted lopinavir using viremia copy‐years
Bibliographic record
Abstract
Introduction HIV‐1 plasma viral load during treatment can be highly variable. Thus, there is the need to find a measure of cumulative viremia that can be used to assess both the short‐ and long‐term efficacy of highly active antiretroviral therapy (HAART). Here, we validate a measure of cumulative viremia to evaluate HAART efficacy. Methods We accessed HAART efficacy using data from a randomized clinical trial conducted in Mexico. We compared the proportion of individuals achieving a viral load <50 and <400 copies/mL at week 48, against the cumulative plasma viral load, estimated as the area under the plasma viral load curve (AUVLC). High AUVLC indicates high cumulative viremia. Results and discussion There was a strong and significant association between the proportion of individuals achieving a viral load <50 and <400 copies/mL at week 48, with individuals suppressed having significant lower cumulative viremia. The median area was 7513 (25th–75th percentile [Q1–Q3] 6634−8180) if viral load <50 copies/mL and 7679 (Q1–Q3 6899−9373) if viral load ≥50 copies/mL ( p ‐value 0.0284). When the analysis was stratified by study arm, individuals on efavirenz had lower cumulative viremia than those on boosted lopinavir. Conclusions Our findings suggest that cumulative viremia should be explored further as a tool to simultaneously evaluate the individual and public health efficacy of HAART. This is particularly relevant to the implementation and evaluation of the Treatment 2.0 strategy recently proposed by UNAIDS and the WHO, as a means to maximize the individual and public health benefit of HAART.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".