Association of CD4+ T-cell Count, HIV-1 RNA Viral Load, and Antiretroviral Therapy With Kaposi Sarcoma Risk Among HIV-infected Persons in the United States and Canada
Bibliographic record
Abstract
BACKGROUND: Kaposi sarcoma (KS) remains common among HIV-infected persons. To better understand KS etiology and to help target prevention efforts, we comprehensively examined a variety of CD4 T-cell count and HIV-1 RNA viral load (VL) measures, as well as antiretroviral therapy (ART) use, to determine independent predictors of KS risk. SETTING: North American AIDS Cohort Collaboration on Research and Design. METHODS: We followed HIV-infected persons during 1996-2009 from 18 cohorts. We used time-updated Cox regression to model relationships between KS risk and recent, lagged, trajectory, and cumulative CD4 count or VL measures, as well as ART use. We used Akaike's information criterion and global P values to derive a final model. RESULTS: In separate models, the relationship between each measure and KS risk was highly significant (P < 0.0001). Our final mutually adjusted model included recent CD4 count [hazard ratio (HR) for <50 vs. ≥500 cells/μL = 12.4; 95% confidence interval (CI): 6.5 to 23.8], recent VL (HR for ≥100,000 vs. ≤500 copies/mL = 3.8; 95% CI: 2.0 to 7.3), and cumulative (time-weighted mean) VL (HR for ≥100,000 vs. ≤500 copies/mL = 2.5; 95% CI: 1.0 to 5.9). Each P-trend was <0.0001. After adjusting for these measures, we did not detect an independent association between ART use and KS risk. CONCLUSIONS: Our results suggested a multifactorial etiology for KS, with early and late phases of development. The cumulative VL effect suggested that controlling HIV replication promptly after HIV diagnosis is important for KS prevention. We observed no evidence for direct anti-KS activity of ART, independent of CD4 count and VL.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".