Clinical utility of biomarkers of endothelial activation and coagulation for prognosis in HIV infection
Bibliographic record
Abstract
INTRODUCTION: HIV infection is associated with vascular dysfunction and adverse cardiovascular outcomes. Our objective was to review the evidence regarding the clinical utility of endothelial activation and coagulation biomarkers for the prognosis of HIV-infected patients. METHODS: We searched PubMed and Embase for publications using the keywords "HIV" or "HIV infection" and "endothelium" or "coagulation". We reviewed reference lists and hand-searched for additional relevant articles. All clinical studies that enrolled non-pregnant, HIV-infected adults, measured biomarkers reflecting endothelial activation or coagulation, and prospectively evaluated their associations with vascular dysfunction or clinical outcomes were included. RESULTS: Seventeen studies were identified that fulfilled the inclusion criteria, of which 11 investigated endothelial activation biomarkers and 12 investigated coagulation biomarkers. Biomarkers and outcomes varied widely across studies. Overall, published studies support an association between P-selectin and venous thromboembolism in HIV-infected patients, an association between tissue-type plasminogen activator and death, and associations between D-dimer and several clinical outcomes, including venous thromboembolism, cardiovascular disease, and all-cause mortality. CONCLUSIONS: Several studies have demonstrated associations between biomarkers of endothelial activation and coagulation and clinically important outcomes in HIV-1 infection. Additional large-scale prospective investigations to determine the utility of endothelial activation and coagulation biomarkers for risk stratification and prediction of adverse outcomes are clearly warranted.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| 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".