Impact of baseline viral load and adherence on survival of HIV-infected adults with baseline CD4 cell counts ≥ 200 cells/μl
Bibliographic record
Abstract
BACKGROUND: Baseline plasma HIV RNA levels > 100 000 copies/ml have been associated with elevated mortality rates after the initiation of HAART. There is uncertainty regarding the optimal strategy for patients with high plasma HIV RNA but CD4 cell count > or = 200 cells/microl. OBJECTIVE: To evaluate the impact of baseline plasma HIV RNA on survival among patients with CD4 cell counts > or = 200 cells/microl. METHODS: Patients were stratified by plasma HIV RNA, CD4 cell count and adherence level. Mortality rates were evaluated using Kaplan-Meier methods and Cox regression. RESULTS: Among 1166 patients initiating HAART with a CD4 cell count > or = 200 cells/microl, a baseline HIV RNA > or = 100 000 copies/ml was statistically associated with elevated mortality among non-adherent patients (log-rank P = 0.032), but not for adherent patients (log-rank P = 0.690). In a multivariate Cox model comparing patients with a baseline CD4 cell count > or = 200 cells/microl and a baseline plasma HIV RNA < 100 000 copies/ml, the mortality rate was statistically similar among patients with a baseline CD4 cell count > or = 200 cells/microl and a baseline plasma HIV RNA > or = 100 000 copies/ml (relative hazard, 1.21; 95% confidence interval, 0.89-1.65; P = 0.232). CONCLUSION: HIV RNA > or = 100 000 copies/ml was only associated with mortality among HIV-infected patients initiating HAART with CD4 cell counts > or= 200 cells/microl if the patients were non-adherent.
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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 | 0.005 |
| 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 | 0.000 | 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".