The Impact of Adherence on CD4 Cell Count Responses Among HIV-Infected Patients
Bibliographic record
Abstract
BACKGROUND: There have been concerns that irreversible immune damage may result if highly active antiretroviral therapy (HAART) is initiated after the CD4 cell count declines to below 350 cells/microL; however, the role of antiretroviral adherence on CD4 cell count responses has not been well evaluated. METHODS: We evaluated CD4 cell count responses of 1522 antiretroviral-naive patients initiating HAART who were stratified by baseline CD4 cell count (<50, 50-199, and >or=200 cells/microL) and adherence. RESULTS: Among patients starting HAART with <50 cells/microL, during the fifth 15-week period after the initiation of HAART, absolute CD4 cell counts were 200 cells/microL (interquartile range [IQR]: 130-290) for adherent patients versus 60 cells/microL (IQR: 10-130) for nonadherent patients. Similarly, among patients starting HAART with 50 to 199 cells/microL, during the fifth 15-week period after the initiation of HAART, absolute CD4 cell counts were 300 cells/microL (IQR: 180-390) versus 125 cells/microL (IQR: 40-210) for nonadherent patients. In Cox regression analyses, adherence was the strongest independent predictor of the time to a gain of >or=50 cells/microL from baseline (relative hazard [RH] = 2.88, 95% confidence interval [CI]: 2.46-3.37). Among patients with baseline CD4 cell counts <200 cells/microL, adherence was the strongest independent predictor of the time to a CD4 cell count >200 cells/microL (RH = 4.85, 95% CI: 3.15-7.47). CONCLUSIONS: These data demonstrate that substantial CD4 gains are possible among highly advanced adherent patients and should contribute to the ongoing debate over the optimal time to initiate 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 | 0.001 | 0.008 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".