Targeting cardiovascular inflammation: next steps in clinical translation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Systemic vascular inflammation plays multiple maladaptive roles which contribute to the progression and destabilization of atherosclerotic cardiovascular disease (ASCVD). These roles include: (i) driving atheroprogression in the clinically stable phase of disease; (ii) inciting atheroma destabilization and precipitating acute coronary syndromes (ACS); and (iii) responding to cardiomyocyte necrosis in myocardial infarction (MI). Despite an evolving understanding of these biologic processes, successful clinical translation into effective therapies has proven challenging. Realizing the promise of targeting inflammation in the prevention and treatment of ASCVD will likely require more individualized approaches, as the degree of inflammation differs among cardiovascular patients. A large body of evidence has accumulated supporting the use of high-sensitivity C-reactive protein (hsCRP) as a clinical measure of inflammation. Appreciating the mechanistic diversity of ACS triggers and the kinetics of hsCRP in MI may resolve purported inconsistencies from prior observational studies. Future clinical trial designs incorporating hsCRP may hold promise to enable individualized approaches. The aim of this Clinical Review is to summarize the current understanding of how inflammation contributes to ASCVD progression, destabilization, and adverse clinical outcomes. We offer forward-looking perspective on what next steps may enable successful clinical translation into effective therapeutic approaches-enabling targeting the right patients with the right therapy at the right time-on the road to more individualized ASCVD care.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it