Inflammatory biomarker levels over 48 weeks with dual vs triple lopinavir/ritonavir-based therapy: Substudy of a randomized trial
Bibliographic record
Abstract
BACKGROUND: Inflammation has been associated with increased morbidity and mortality in HIV-positive patients. We compared inflammatory biomarkers with dual therapy using lopinavir/ritonavir plus lamivudine (LPV/r+3TC) versus triple therapy using LPV/r plus two nucleoside reverse transcriptase inhibitors (LPV/r+2NRTIs) in treatment-naïve HIV-positive adults. METHODS: This was a substudy among Argentinian participants in the randomized trial GARDEL. We measured hsCRP, IL-6, MCP-1, TNF, D-dimer and sCD14 from plasma collected at baseline, week 24 and week 48. Generalized estimating equations with an identity/logit link were used to model the average impact of dual versus triple therapy on each biomarker over time, controlling for baseline levels. Additional models estimated the average effect of virologic suppression on biomarker levels over time, adjusting for age, sex, and baseline CD4 count. RESULTS: Of 191 trial participants enrolled in Argentina, 172 had baseline and follow-up measurements and were included. Median (IQR) age was 35.5 (28.5, 45) years and CD4 cell count was 310 (219, 414) cells/mm3. Dual therapy was not associated with significantly different biomarker levels over 48 weeks relative to triple therapy. Virologic suppression was associated with statistically significant decreases in MCP-1, TNF and D-dimer levels and an unexpected increase in sCD14 levels. No change was observed in hsCRP or the proportion of participants with undetectable IL-6 levels. CONCLUSIONS: In addition to having virologic non-inferiority, LPV/r+3TC dual therapy is generally associated with similar inflammatory biomarker levels over 48 weeks compared to LPV/r+2NRTIs triple therapy in treatment-naïve adults. Further study of dual treatment regimens is warranted.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".