No differences in clinical outcomes with the addition of viral load testing to CD4 cell count monitoring among HIV infected participants receiving ART in rural Uganda: Long-term results from the Home Based AIDS Care Project
Bibliographic record
Abstract
BACKGROUND: We compared clinical outcomes among HIV-infected participants receiving ART who were randomized to viral load (VL) and CD4 cell count monitoring in comparison to CD4 cell count monitoring alone in Tororo, Uganda. METHODS: Beginning in May 2003, participants with CD4 cell counts <250 cells/μL or WHO stage 3 or 4 disease were randomized to clinical monitoring alone, clinical monitoring plus quarterly CD4 cell counts (CD4-only); or clinical monitoring, quarterly CD4 cell counts and quarterly VL testing (CD4-VL). In 2007, individuals in clinical monitoring arm were re-randomized to the other two arms and all participants were followed until March 31, 2009. We used Cox Proportional Hazard models to determine if study arm was independently associated with the development of opportunistic infections (OIs) or death. RESULTS: We randomized 1211 participants to the three original study arms and 331 surviving participants in the clinical monitoring arm were re-randomized to the CD4-VL and CD4 only arms. At enrolment the median age was 38 years and the median CD4 cell count was 134 cells/μL. Over a median of 5.2 years of follow-up, 37 deaths and 35 new OIs occurred in the VL-CD4 arm patients, 39 deaths and 42 new OIs occurred in CD4-only patients. We did not observe an association between monitoring arm and new OIs or death (AHR =1.19 for CD4-only vs. CD4-VL; 95 % CI 0.82-1.73). CONCLUSION: We found no differences in clinical outcomes associated with the addition of quarterly VL monitoring to quarterly CD4 cell count monitoring.
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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 | 0.017 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".