Baseline CD4 Count and Adherence to Antiretroviral Therapy: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: In light of recent changes to antiretroviral treatment (ART) guidelines of the World Health Organization and ongoing concerns about adherence with earlier initiation of ART, we conducted a systematic review of published literature to review the association between baseline (pre-ART initiation) CD4 count and ART adherence among adults enrolled in ART programs worldwide. METHODS: We performed a systematic search of English language original studies published between January 1, 2004 and September 30, 2015 using Medline, Web of Science, LILACS, AIM, IMEMR, and WPIMR databases. We calculated the odds of being adherent at higher CD4 count compared with lower CD4 count according to study definitions and pooled data using random effects models. RESULTS: Twenty-eight articles were included in the review and 18 in the meta-analysis. The odds of being adherent was marginally lower for patients in the higher CD4 count group (pooled odds ratio, 0.90; 95% confidence interval, 0.84 to 0.96); however, the majority of studies found no difference in the odds of adherence when comparing CD4 count strata. In analyses restricted to comparisons above and below a CD4 count of 500 cells per microliter, there was no difference in adherence (pooled odds ratio, 1.01; 95% confidence interval: 0.97 to 1.05). CONCLUSIONS: This review was unable to find consistent evidence of differences in adherence according to baseline CD4 count. Although this is encouraging for the new recommendations to treat all HIV-positive individuals irrespective of CD4 count, there is a need for additional high-quality studies, particularly among adults initiating ART at higher CD4 cell counts.
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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 | 0.013 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.032 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".