Effect of the ratio of coronary arterial lumen volume to left ventricle myocardial mass derived from coronary CT angiography on fractional flow reserve
Bibliographic record
Abstract
BACKGROUND: We hypothesize that in patients with suspected coronary artery disease (CAD), lower values of the ratio of total epicardial coronary arterial lumen volume to left ventricular myocardial mass (V/M) result in lower fractional flow reserve (FFR). METHODS: V/M was computed in 238 patients from the NXT trial who underwent coronary computed tomography angiography (CTA), quantitative coronary angiography (QCA) and FFR measurement in 438 vessels. Nitroglycerin was administered prior to CT, QCA and FFR acquisition. The V/M ratio was quantified on a patient-level from CT image data by segmenting the epicardial coronary arterial lumen volume (V) and the left ventricular myocardial mass (M). Calcified and noncalcified plaque volumes were quantified using semi-automated software. RESULTS: /g) was used to define equal groups of low and high V/M patients. Patients with low V/M had greater diameter stenosis by QCA, more plaque and lower FFR (0.80 ± 0.12 vs. 0.87 ± 0.08; P < 0.0001) than those with high V/M. A total of 365 vessels in 202 patients had QCA stenosis ≤50% and measured FFR. In these patients, those with low V/M had higher percent diameter stenosis by QCA, greater total plaque volume and lower FFR (0.81 ± 0.12 vs. 0.88 ± 0.07; P < 0.0001) than those with high V/M. In multivariate logistic regression analysis, V/M was an independent predictor of FFR ≤0.80 (all p-values < 0.001). CONCLUSIONS: Patients with a low V/M ratio have lower FFR overall and in non-obstructive CAD, independent of plaque measures.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".