Why are baseline HIV RNA levels 100,000 copies/mL or greater associated with mortality after the initiation of antiretroviral therapy?
Bibliographic record
Abstract
BACKGROUND: There is conflicting evidence regarding the impact of baseline plasma HIV RNA on virologic responses after the initiation of triple-drug antiretroviral therapy (highly active antiretroviral therapy [HAART]). This has made it difficult to interpret the recently reported association between baseline plasma HIV RNA and mortality. We evaluated whether baseline CD4 cell count and plasma HIV RNA predicted virologic suppression (<500 copies/mL) and rebound (> or =500 copies/mL) among adherent HIV-infected patients. METHODS: Antiretroviral-naive HIV-infected patients were stratified by baseline CD4 cell count, plasma HIV RNA, and adherence level. Cox and logistic regression were used to evaluate the time to suppression and rebound and the odds of ever achieving HIV RNA suppression. RESULTS: A total of 1422 individuals initiated HAART between August 1, 1996 and July 31, 2000 and were followed to March 31, 2002. Adherent patients with HIV RNA levels > or =100,000 copies/mL and 50 to 99,999 copies/mL were slower to suppress HIV RNA than patients with baseline HIV RNA <50,000 copies/mL in Kaplan-Meier analyses. Although the odds of RNA suppression among adherent patients with baseline RNA levels <50,000 copies/mL and 50 to 99,999 copies/mL were similar (P = 0.197), patients with baseline HIV RNA > or =100,000 copies/mL were markedly less likely ever to achieve suppression during follow-up (adjusted odds ratio: 0.27 [95% confidence interval: 0.13-0.54]; P < 0.001). No differences in the rate of virologic rebound were observed between adherent patients in the various baseline HIV RNA strata, and CD4 cell count was not associated with suppression or rebound. CONCLUSIONS: Baseline HIV RNA > or =100,000 copies/mL was associated with a significantly lower likelihood of ever achieving HIV RNA suppression during follow-up. These findings likely explain the association between baseline HIV RNA levels and mortality and have important implications for the development of therapeutic 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 | 0.002 | 0.015 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".