The Impact of Adherence on CD4 Cell Count Responses Among HIV-Infected Patients
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 it