Virologic and immunologic response to HAART, by age and regimen class
Bibliographic record
Abstract
OBJECTIVE: To determine the impact of age and initial HAART regimen class on virologic and immunologic response within 24 months after initiation. DESIGN: Pooled analysis of data from 19 prospective cohort studies in the North American AIDS Cohort Collaboration on Research and Design (NA-ACCORD). METHODS: Twelve thousand, one hundred and ninety-six antiretroviral-naive adults who initiated HAART between 1998 and 2008 using a boosted protease inhibitor-based regimen or a nonnucleoside reverse transcriptase inhibitor (NNRTI)-based regimen were included in our study. Discrete time-to-event models estimated adjusted hazard odds ratios (aHOR) and 95% confidence intervals (CIs) for suppressed viral load (≤500 copies/ml) and, separately, at least 100 cells/μl increase in CD4 cell count. Truncated, stabilized inverse probability weights accounted for selection biases from discontinuation of initial regimen class. RESULTS: Among 12 196 eligible participants (mean age = 42 years), 50% changed regimen classes after initiation (57 and 48% of whom initiated protease inhibitor and NNRTI-based regimens, respectively). Mean CD4 cell count at initiation was similar by age. Virologic response to treatment was less likely in those initiating using a boosted protease inhibitor [aHOR = 0.77 (0.73, 0.82)], regardless of age. Immunologic response decreased with increasing age [18-<30: ref; 30-<40: aHOR = 0.92 (0.85, 1.00); 40-<50: aHOR = 0.85 (0.78, 0.92); 50-<60: aHOR = 0.82 (0.74, 0.90); ≥60: aHOR = 0.74 (0.65, 0.85)], regardless of initial regimen. CONCLUSION: We found no evidence of an interaction between age and initial antiretroviral regimen on virologic or immunologic response to HAART; however, decreased immunologic response with increasing age may have implications for age-specific when-to-start guidelines.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".