Improved diagnostic accuracy when combining computed tomography angiography and corrected coronary opacification in patients with coronary stents
Bibliographic record
Abstract
Background The accuracy of coronary computed tomography angiography (CCTA) is sub-optimal in patients with coronary stents. Methods that can increase its diagnostic accuracy are desirable. Objective A proof-of-concept study was undertaken to determine if corrected coronary opacification (CCO) differences can improve the accuracy of CCTA in stented coronary arteries. Methods PCI patients who underwent both CCTA and invasive coronary angiography (ICA) within 3 months were analyzed. Coronary luminal attenuation values (normalized to the aorta) were measured proximally and distally to coronary stents. CCO differences were evaluated for ability to predict (i) stenosis on invasive angiography, (ii) abnormal resting coronary flow as measured by the corrected thrombolysis in myocardial infarction (TIMI) frame count (cTFC), and (iii) the combination of abnormal resting flow and significant stenosis on invasive angiography. Results Twenty-nine stented coronary arteries (n = 25, mean age =61.4 years, men =80.0%) were assessed. In stented coronary vessels, CCO identified stenosis (≥ 70%) with an area under the curve of 0.767 (P = 0.021). CCO predicted abnormal resting coronary flow with high accuracy (AUC =0.867, P = 0.002). Combined CCTA/CCO identified both abnormal cTFC and stenosis ≥70% on ICA (functionally significant stenosis) with diagnostic accuracy of 92.3%. In contrast, CCTA visual assessment alone had lower diagnostic accuracy of 66.7% for identifying functionally significant stenosis. Conclusion CCO differences are predictive of abnormal resting flow and consequently, in-stent restenosis. Incorporation of this technique may improve the specificity of CCTA in PCI patients.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".