256-Slice CT Angiographic Evaluation of Coronary Artery Bypass Grafts: Effect of Heart Rate, Heart Rate Variability and Z-Axis Location on Image Quality
Bibliographic record
Abstract
PURPOSE: The objective of this study is to assess the effect of heart rate, heart rate variability and z-axis location on coronary artery bypass graft (CABG) image quality using a 256-slice computed tomography (CT) scanner. METHODS: A total of 78 patients with 254 CABG (762 graft segments) were recruited to undergo CABG assessment with 256-slice CT and prospective ECG-gating. Two observers rated graft segments for image quality on a 5-point scale. Quantitative measurements were also made. Logistic and cumulative link mixed models were used to assess the predictors of graft image quality. RESULTS: Graft image quality was judged as diagnostic (scores 5 (excellent), 4 (good) and 3 (moderate)) in 96.6% of the 762 segments. Interobserver agreement was excellent (kappa ≥0.90). Graft image quality was not affected by heart rate level. However, high heart rate variability was associated with an important and significant image quality deterioration (odds ratio 4.31; p = 0.036). Distal graft segments had significantly lower image quality scores than proximal segments (p ≤ 0.02). Significantly higher noise was noted at the origin of the mammary grafts (p = 0.001), owing to streak artifacts from the shoulders. CONCLUSION: CABG imaging with 270-msec rotation 256-slice CT and prospective ECG-gating showed an adequate image quality in 96.6% of graft segments, and an excellent interobserver agreement. Graft image quality was not influenced by heart rate level. Image quality scores were however significantly decreased in patients with high heart rate variability, as well as in distal graft segments, which are closer to the heart.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.001 | 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".