The Association Between HIV Infection and Carotid Intima-Media Thickness in the Era of Antiretroviral Therapy: A Meta-Analysis
Bibliographic record
Abstract
Atherosclerosis remains a leading cause of mortality globally, and this is worse in people living with HIV (PLHIV). While the administration of antiretroviral therapy (ART) in this population has significant benefits, it is essential to acknowledge that it also has some undesired effects. This study investigated the impact of ART on carotid intima-media thickness (CIMT) in PLHIV as a marker of early atherosclerosis. A literature search was conducted on the PubMed, Scopus, and EBSCOhost databases from 1 January 1987 to 30 May 2025. The methodological quality of the studies was assessed using the Newcastle–Ottawa scale. Data were analyzed using a meta-analysis web tool and reported as the mean difference (MD) and 95% confidence intervals (CIs). Twenty-seven studies, which included 3250 PLHIV on ART and 1542 who were ART-naive, were relevant. The mean age was 41.26 in ART and 39.91 years. The results showed a higher CIMT in PLHIV on ART compared to the ART-naive group, MD = 0.03 mm, 95% CI (0.02 mm to 0.04 mm), p < 0.0001; I2 = 96.9%. Subgroup analysis showed that the inclusion of studies conducted on male participants only, those with a sample size of one hundred, and those with a moderate risk of bias contributed to heterogeneity. The results suggest there is an increased risk of atherosclerosis in PLHIV on ART.
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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 | 0.014 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.069 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".