CD4 percentage is an independent predictor of survival in patients starting antiretroviral therapy with absolute CD4 cell counts between 200 and 350 cells/μL
Bibliographic record
Abstract
OBJECTIVE: To determine the prognostic value of baseline CD4 percentage in terms of patient survival in comparison to absolute CD4 cell counts for HIV-positive patients initiating highly active antiretroviral therapy (HAART). METHODS: A population-based cohort study of 1,623 antiretroviral therapy-naïve HIV-positive individuals who initiated HAART between 1 August 1996 and 30 June 2002 was conducted. Cumulative mortality rates were estimated using Kaplan-Meier methods. Cox proportional hazards regression was used to model the effect of baseline CD4 strata and CD4 percentage strata and other prognostic variables on survival. A subgroup analysis was conducted on 417 AIDS-free subjects with baseline CD4 counts between 200 and 350 cells/microL. RESULTS: In multivariate models, low CD4 percentages were associated with increased risk of death [CD4%<5, relative hazard (RH)=4.46; CD4% 5-14, RH=2.43; P<0.01 for both] when compared with those subjects with an initial CD4 fraction of 15% or greater, but had less predictive value than absolute CD4 counts. In subgroup analyses where absolute CD4 strata were not associated with mortality, a baseline CD4 fraction below 15% [RH=2.71; 95% confidence interval (CI) 1.20-6.10], poor adherence to therapy and baseline viral load >100,000 HIV-1 RNA copies/mL were associated with an increased risk of death. CONCLUSION: CD4 percentages below 15% are independent predictors of mortality in AIDS-free patients starting HAART, including those with CD4 counts between 200 and 350 cells/microL. CD4 percentage should be considered for inclusion in guidelines used to determine when to start therapy.
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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 | 0.000 | 0.003 |
| 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".