Predictors for Vulnerable Plaque in Functionally Significant Lesions
Bibliographic record
Abstract
BACKGROUND: Vulnerable plaque presents prognostic implications in addition to functional significance. OBJECTIVES: The aim of this study was to identify relevant features of vulnerable plaque in functionally significant lesions. METHODS: In this multicenter, prospective study conducted across 5 countries, including patients who had invasive fractional flow reserve (FFR) ≤0.80, a total of 95 patients with available pullback pressure gradient (PPG) and plaque analysis on coronary computed tomographic angiography and optical coherence tomography were analyzed. Vulnerable plaque was defined as the presence of plaque rupture or thin-cap fibroatheroma on optical coherence tomography. Among the 25 clinical characteristics, invasive angiographic findings, physiological indexes, and coronary computed tomographic angiographic findings, significant predictors of vulnerable plaque were identified. RESULTS: Mean percentage diameter stenosis, FFR, and PPG were 77.8% ± 14.6%, 0.66 ± 0.13, and 0.65 ± 0.13, respectively. Vulnerable plaque was present in 53 lesions (55.8%). PPG and FFR were identified as significant predictors of vulnerable plaque (P < 0.05 for all). PPG >0.65 and FFR ≤0.70 were significantly related to a higher probability of vulnerable plaque after adjustment for each other (OR: 6.75 [95% CI: 2.39-19.1]; P < 0.001] for PPG >0.65; OR: 4.61 [95% CI: 1.66-12.8]; P = 0.003 for FFR ≤0.70). When categorizing lesions according to combined PPG >0.65 and FFR ≤0.70, the prevalence of vulnerable plaque was 20.0%, 57.1%, 66.7%, and 88.2% in the order of PPG ≤0.65 and FFR >0.70, PPG ≤0.65 and FFR ≤0.70, PPG >0.65 and FFR >0.70, and PPG >0.65 and FFR ≤0.70 (P for trend < 0.001), respectively. CONCLUSIONS: Among low-FFR lesions, the presence of vulnerable plaque can be predicted by PPG combined with FFR without additional anatomical or plaque characteristics. (Precise Percutaneous Coronary Intervention Plan [P3] Study; NCT03782688).
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".