<i>Review</i> : Immunologic Response to Protease Inhibitor-Based Highly Active Antiretroviral Therapy: A Review
Bibliographic record
Abstract
A substantial body of evidence indicates that CD4 cell count is an important independent prognostic indicator for progression of HIV disease. Consequently, in addition to plasma HIV RNA levels, CD4 cell count change is considered to be a key surrogate marker for disease progression in clinical practice and in clinical studies. Given the relationship between changes in CD4 count and disease progression, it is notable that protease inhibitor (PI)-based highly active antiretroviral therapy (HAART) can rapidly increase CD4 cell count early in treatment in both therapy-naïve and -experienced patients, and can sustain clinically relevant levels beyond 24 weeks. A number of trials with a follow-up of more than 3 years allow us to conclude that the gains in CD4 counts are maintained in a durable manner. This review evaluated randomized studies of PI-based and PI-boosted HAART (published between January 1996 and February 2006) to determine the effect of PI-based therapy on CD4 cell count. Only studies that assessed CD4 response in the overall patient population were included. Four mechanisms have been proposed to account for the rapid increase in CD4 cell count that occurs with HAART: CD4 cell redistribution from lymphatic tissues, increased CD4 cell production, reduction of apoptotic CD4 cells and the recovery of hematopoietic activity in bone marrow. Further research is required to clarify the relative importance of these mechanisms and ways in which they might be enhanced.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".