First-line cART regimen impacts the course of CD8+ T-cell counts in HIV-infected patients that achieve sustained undetectable viral load.
Bibliographic record
Abstract
The aim of the study was to investigate the impact of first-line combined antiretroviral therapy (cART) regimen on the course of CD8 T-cell counts in human immunodeficiency virus (HIV)-infected patients.A retrospective observational study conducted on the French DAT'AIDS Cohort of HIV-infected patients.We selected 605 patients initiating a first-line cART between 2002 and 2009, and which achieved a sustained undetectable HIV plasma viral load (pVL) for at least 12 months without cART modification. The evolution of CD8 T-cell counts according to cART regimen was assessed.CD8 T-cell counts were assessed in 572 patients treated with 2NRTIs+1PI/r (n= 297), 2NRTIs+1NNRTI (n= 207) and 3NRTIs (n= 68). In multivariate analysis, after 12 months of follow-up, the 3NRTIs regimen was associated with a significantly smaller decrease of CD8 T-cell count compared with NNRTI-containing regimens (-10.2 cells/μL in 3NRTIs vs -105.1 cells/μL; P=0.02) but not compared with PI-containing regimens (10.2 vs -60.9 cells/μL; P=0.21). After 24 months, the 3NRTIs regimen was associated with a smaller decrease of CD8 T-cell count and % compared with PI/r- and NNRTI-containing regimens (0.2 in 3NRTIs vs -9.9 with PI/r-regimens, P=0.001, and vs -11.1 with NNRTI-regimens, p < 0.0001). A focus analysis on 11 patients treated with an INSTI-containing cART regimen during the study period showed after 12 months of follow-up, a median decrease of CD8 T-cell count of -155 [inter quartile range: -302; -22] cells/μL.Our data highlight the fact that cART regimens have differential effects on CD8 pool down regulation.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".