Comparison of Prognostic Importance of Latest CD4+ Cell Count and HIV RNA Levels in Patients with Advanced HIV Infection on Highly Active Antiretroviral Therapy
Bibliographic record
Abstract
UNLABELLED: The comparative prognostic importance of latest plasma HIV RNA levels (viral loads) and CD4+ cell counts among patients prescribed highly active antiretroviral therapy (HAART) has not been well characterized. METHOD: We assessed the prognostic value of latest CD4+ cell counts and latest viral loads for progression to AIDS or death and explored their interaction among 432 HIV-infected persons with advanced HIV who were prescribed a protease inhibitor (PI) as their first HAART regimen. RESULTS: Pre-HAART median CD4+ cell count and viral load were 41 cells/mm3 and 126,331 copies/mL, respectively. After 12 months of HAART, the median CD4+ cell count was 154 cells/mm3; 39% of patients had a viral load of 400 copies/mL or lower. Over a median follow-up of 33 months, 109 (25%) of the 432 patients experienced an AIDS event or died. The hazard ratio for AIDS or death for those with latest CD4+ cell count <50 cells/mm3 versus > or =200 cells/mm3 was 13.9 (95% CI 6.5 to 29.7) without adjustment for latest viral load measurements and 9.5 (95% CI 4.0 to 22.5) after adjustment for latest viral load. In contrast, the hazard ratio for AIDS or death for those with viral load > or =100,000 versus <400 copies/mL was 4.2 (95% CI 2.3 to 7.7) without adjustment for latest CD4+ level and 1.2 (95% CI 0.6 to 2.4) with adjustment for latest CD4+ cell count. CONCLUSION: We conclude that when latest CD4+ cell count and viral load are considered separately, both are significantly related to AIDS or death; when these markers are jointly considered, the association of viral load with AIDS or death is substantially diminished. Latest CD4+ levels are more strongly related to AIDS or death than latest viral load levels in patients on HAART.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".