Comparative CD4 T-Cell Responses of Reverse Transcriptase Inhibitor Therapy With or Without Nelfinavir Matched for Viral Exposure
Bibliographic record
Abstract
BACKGROUND: Therapy of HIV infection with protease inhibitors (PIs) may be associated with improvements in CD4 T-cell number via a mechanism that is independent of effects on plasma viral load (VL). PURPOSE: To compare CD4 responses of patients who receive reverse transcriptase inhibitor (RTI) therapies with or without a PI, matched for viral exposure. METHODS: Patient data were analyzed from two prospective randomized trials of antiviral therapy with or without nelfinavir. Total viral exposure over 24 weeks was estimated by viral area under the curve (AUC), which reflects baseline viral load, slope of virologic decay, viral nadir, and duration of suppression. Patients were stratified into quartiles on the basis of viral AUC, and CD4 T-cell responses were evaluated between PI-containing and RTI-only treatment groups within each quartile. RESULTS: In both trials, patients receiving nelfinavir had greater CD4 T-cell increases than patients receiving RTI alone. Analysis of variance modeling revealed increased CD4 T-cell responses in PI-treated groups at all time points after the second week. These differences were significant (p <.05) at weeks 12, 24, 28, 32, 36, 40, and 48 in one study, and weeks 1, 2, 4, 6, 8, 12, 16, 20, 24, 28, 32, 36, and 44 in the other. Within quartiles matched for viral AUC, absolute CD4 T-cell change from baseline was greater in the PI-treated patients at 84% (101/120) of time points analyzed. CONCLUSION: Nelfinavir-containing therapy is associated with enhanced increases in CD4 T-cell number compared to RTI therapy alone with equivalent antiviral effect. These data suggest that PIs influence CD4 T-cell number through a nonvirologic effect.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".