Off-pump Versus On-pump Coronary Artery Bypass Surgery: Graft Patency Assessment With Coronary Computed Tomographic Angiography
Bibliographic record
Abstract
PURPOSE: A large multicenter randomized trial (RCT) is needed to assess off-pump coronary artery bypass graft (CABG) patency when performed by skilled surgeons. This prospective multicenter randomized pilot study compares graft patency after on-pump and off-pump techniques and addresses the feasibility of such an RCT. MATERIALS AND METHODS: Consecutive patients were prospectively recruited for ≥64-slice computed tomography angiography graft patency assessment 1 year after randomization to off-pump or on-pump CABG. Blinded assessment of graft patency was performed, and the results were categorized as normal, ≥50% stenosis, or occlusion. A multilevel model with random effects on the patient was used to account for correlation of results in patients with multiple grafts. RESULTS: A total of 157 patients (3 centers, 84 off-pump and 73 on-pump patients, 512 grafts, assessability rate 98.4%) were included. Patency index (% nonoccluded grafts) was 89% for the off-pump technique and 95% for the on-pump technique (P=0.09). Patency was similar for arterial and vein grafts (both 92%; P=0.88), as well as between target territories (89% to 94%; P=0.53). CONCLUSIONS: In this pilot study, 1-year graft patency results after off-pump and on-pump surgery were similar. This feasibility trial demonstrates that a large multicenter RCT to compare CABG patency after on-pump with that after off-pump techniques is feasible and can be reliably undertaken using computed tomography angiography.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".