Biomarkers of in vivo platelet activation in coronary artery disease: a systematic review and meta-analysis: communication from the SSC of the ISTH
Bibliographic record
Abstract
BACKGROUND: Given the role of platelets in coronary artery disease (CAD), assessment of a soluble platelet-activation marker may be useful to improve thrombotic risk stratification. OBJECTIVES: This study aimed to perform a meta-analysis investigating the association between levels of 14 such markers associated with CAD. METHODS: , β-thromboglobulin, soluble CD40L (sCD40L), glycocalicin, glycoprotein (GP)V, GPVI, matrix metalloproteinase (MMP)-9 and MMP-2, platelet factor (PF)4, soluble (s) P-selectin, SCUBE1, serotonin and thrombospondin (TSP)-1 between patients with CAD and healthy subjects (HSs) in plasma and/or serum. When possible, patients with CAD were stratified into acute coronary syndrome (ACS) and chronic coronary disease. Standardized mean difference (SMD) was calculated. RESULTS: Due to heterogeneity in the assessed studies, meta-analysis was performed for sCD40L, soluble GPV, MMP-9, PF4, sP-selectin, SCUBE1, and TSP-1. All markers but TSP-1 were significantly elevated in patients with CAD compared with HSs. Differences in sCD40L and SCUBE1 were statistically significant only when studies that assessed plasma were combined with those that assessed serum. When compared with HSs, the differences were bigger in patients with ACS than patients with chronic coronary disease for MMP-9 (SMD, 2.49 vs 0.49), PF4 (SMD, 2.01 vs 0.96), and sP-selectin (SMD, 1.81 vs 0.63). Publication bias was identified for sCD40L and, in ACS, for sP-selectin and PF4. CONCLUSION: The increased levels of sCD40L, soluble GPV, MMP-9, PF4, sP-selectin, and SCUBE1 in patients with CAD compared with HSs provide a rationale for designing new studies to address the potential of such molecules as biomarkers for thrombotic risk stratification.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.015 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".