P864Diagnostic performance of computed tomography derived fractional flow reserve on functional ischemia of coronary stenosis in each culprit vessel
Bibliographic record
Abstract
Background: Fractional flow reserve (FFR) is the gold standard for identifying functional severity of coronary artery disease (CAD). Although computation of FFR from coronary computed tomography angiography (FFRct) recently provided high diagnostic accuracy for identifying functional lesion severity, it was unclear whether those were similar in each vessel. Purpose: The purpose of this study was to evaluate the diagnostic performance of FFRct between left anterior descending artery (LAD) and non-LAD. Methods: We prospectively enrolled stable CAD patients with 47 lesions which were performed both FFRct and invasive FFR measurements. Functional ischemia was defined as FFR ≤0.80. Results: In this subjects, 26 lesions were distributed in LAD. FFRct showed a good correlation with invasive FFR (r=0.71, p<0.01). From the ROC curve analysis, the diagnostic accuracy of FFRct was 81% (AUC 0.87, sensitivity 89%, specificity 76%) in overall subjects. The diagnostic accuracy was higher in LAD than in non-LAD (88% vs. 52%). The Bland-Altman plot between FFRct and invasive FFR demonstrated better agreement with a mean difference of 0.019 and a standard deviation of 0.063 in LAD compared to a mean difference of 0.108 and 0.073 in non-LAD.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".