The effect of opportunistic illness on HIV RNA viral load and CD4+ T cell count among HIV‐positive adults taking antiretroviral therapy
Bibliographic record
Abstract
INTRODUCTION: HIV RNA viral load (VL) has been shown to increase during opportunistic illnesses (OIs), suggesting active HIV replication in response to infection among patients not taking antiretroviral therapy (ART). We assessed the effects of OIs on HIV RNA VL and CD4+ T cell counts among patients on ART with initially suppressed VL. METHODS: Between 2003 and 2007, we enrolled and followed 1094 HIV-1-infected adults who initiated ART and had quarterly blood draws for VL and CD4+ T cell count. In VL/CD4+ T cell measurement intervals following undetectable VL, we compared the elevation in VL to detectable levels and CD4+ T cell count changes between intervals when participants had episodes of OIs and intervals when they did not have OIs. RESULTS: VL was more likely to be detectable if participants had OIs in the prior three months compared to when they did not (OR=4.0 (95% CI=1.9-8.6)). The CD4+ T cell counts declined 24.1 cells/µL per three months in intervals where the participants had OIs compared to an increase of 21.3 cells/µL per three months in intervals where they did not have OIs (adjusted difference in the rate of CD4+ T cell count change of 61.7 cells/µL per three months (95% CI=13.7-109.7), P value=0.012). The rate of CD4+ T cell count increase was 25.6 cells/µL per three months (95% CI=11.6-39.6) higher for females compared to males (p value=<0.001), 1.4 cells/µL per three months lower per one year increase in age (p value=0.046) and 4 cells/µL per three months lower per 10 cells/µL increase in the starting CD4+ T cell count value (p value=<0.001). CONCLUSION: Episodes of opportunistic infections among patients taking ART with undetectable VL were associated with elevation of HIV RNA VL to detectable levels and decline in CD4+ T cell counts. CLINICAL TRIAL NUMBER: NCT00119093.
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 | 0.005 |
| 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) | 0.002 | 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".