Inflammatory Pathways in Coronary Artery Disease: Which Ones to Target for Secondary Prevention?
Bibliographic record
Abstract
Coronary artery disease (CAD), the build-up of atherosclerotic plaques on the wall of blood vessels, causes adverse cardiovascular events. Secondary prevention focuses on treating patients with existing plaques to prevent disease progression. Recent studies have shown that inflammation is an independent risk factor that drives disease progression, and targeting inflammation could be an effective therapeutic strategy for secondary prevention. In this review, we highlighted the roles of several inflammatory pathways in rupture and erosion, two major processes through which established plaques lead to adverse cardiovascular events. In the past 15 years, numerous clinical trials have tested the therapeutic potential of targeting these pathways, including neutralizing inflammatory cytokines and blocking signaling transduction of the inflammatory pathways. Only colchicine was approved for clinical use in patients with CAD. This is primarily due to the multifaceted roles of inflammatory pathways in disease progression. Commonly used pre-clinical models provided robust information for the onset of early disease but limited understanding of the inflammatory network in established plaques. This review will summarize lessons learned from successful and failed clinical trials to advocate for assessing inflammation in established plaques before designing therapeutics for secondary prevention.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".