P2486Differential association between the progression of coronary artery calcium and coronary plaque volume progression according to statins
Bibliographic record
Abstract
Introduction: Coronary artery calcium (CAC) is a strong predictor of major adverse cardiac events (MACE). On the other hand, statins, which markedly reduces risk of MACE and attenuates the CAD progression, increases CAC. To date, the relation between CAC and coronary plaque volume (PV) were not fully characterized, and it is still unknown if CAC progression reflects the PV progression regardless of the use of statins. Purpose: To explore whether the association between CAC progression and PV progression differs according to statin. Methods: We performed a prospective multinational registry of consecutive patients who underwent serial CCTA at ≥2-year interval. Coronary PV (total, calcified, and non-calcified; sum of fibrous, fibro-fatty, and necrotic-core) were quantitatively analysed. Multivariate linear regression models were constructed for each statin-taking and statin-naïve group. Results: In total 654 patients (61±10 years, 56% male, inter-scan interval 3.9±1.5 years), there were 246 statin-naïve and 408 statin-taking patients. In multivariate analysis (Table), all annualized total PV (β=1.20; p<0.001), non-calcified PV (β=0.39, p=0.020), and calcified PV (β=2.40, p<0.001) progression were independently associated with CAC progression in statin-naïve group. However, in statin-taking group, only total PV (β=0.91, p<0.001) and calcified PV (β=1.74, p<0.001) progression were associated with CAC progression, but not non-calcified PV (β=-0.191, p=0.098).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".