HIV-related cardiovascular diseases: the search for a unifying hypothesis
Bibliographic record
Abstract
Although the extensive rollout of antiretroviral (ARV) therapy resulted in a longer life expectancy for people living with human immunodeficiency virus (PLHIV), such individuals display a relatively increased occurrence of cardiovascular diseases (CVD). This health challenge stimulated significant research interests in the field, leading to an improved understanding of both lifestyle-related risk factors and the underlying mechanisms of CVD onset in PLHIV. However, despite such progress, the precise role of various risk factors and mechanisms underlying the development of HIV-mediated CVD still remains relatively poorly understood. Therefore, we review CVD onset in PLHIV and focus on 1) the spectrum of cardiovascular complications that typically manifest in such persons and 2) underlying mechanisms that are implicated in this process. Here, the contributions of such factors and modulators and underlying mechanisms are considered in a holistic and integrative manner to generate a unifying hypothesis that includes identification of the core pathways mediating CVD onset. The review focuses on the sub-Saharan African context, as there are relatively high numbers of PLHIV residing within this region, indicating that the greater CVD risk will increasingly threaten the well-being and health of its citizens. It is our opinion that such an approach helps point the way for future research efforts to improve treatment strategies and/or lifestyle-related modifications for PLHIV.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".