Murray law-based quantitative flow ratio to assess left main bifurcation stenosis: selecting the angiographic projection matters
Bibliographic record
Abstract
Abstract Murray law-based quantitative flow ratio (µQFR) assesses fractional flow reserve (FFR) in bifurcation lesions using a single angiographic view, enhancing the feasibility of analysis; however, accuracy may be compromised in suboptimal angiographic projections. FFR CT is a well-validated non-invasive method measuring FFR from coronary computed tomographic angiography (CCTA). We evaluated the feasibility of µQFR in left main (LM) bifurcations, the impact of the optimal/suboptimal fluoroscopic view with respect to CCTA, and its diagnostic concordance with FFR CT . In 300 patients with three-vessel disease, the values of FFR CT and µQFR were compared at distal LM, proximal left anterior descending artery (pLAD) and circumflex artery (pLCX). The optimal viewing angle of LM bifurcation was defined on CCTA by 3-dimensional coordinates and converted into a 2-dimensional fluoroscopic view. The best fluoroscopic projection was considered the closest angulation to the optimal viewing angle on CCTA. µQFR was successfully computed in 805 projections. In the best projections, µQFR sensitivity was 88.2% (95% CI 76.1–95.6) and 84.8% (71.1–93.7), and specificity was 96.8% (93.8–98.6) and 97.2% (94.4–98.9), in pLAD and pLCX, respectively, with regard to FFR CT . The AUC of µQFR for predicting FFR CT ≤ 0.80 tended to be improved using the best versus suboptimal projections (0.94 vs. 0.89 [p = 0.048] in pLAD; 0.94 vs. 0.88 [p = 0.075] in pLCX). Computation of µQFR in LM bifurcations using a single angiographic view showed high feasibility from post-hoc analysis of coronary angiograms obtained for clinical purposes. The fluoroscopic viewing angle influences the diagnostic performance of physiological assessment using a single angiographic view.
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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 | 0.002 | 0.000 |
| 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 | 0.000 |
| 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".