Abstract 23242: SOMVC001(DuraGraft) Vascular Graft Treatment in Patients Undergoing Coronary ArteryBypass Grafting
Bibliographic record
Abstract
Background: The principal mechanism thought to limit the benefits of CABG is saphenous vein graft disease leading to vein graft failure (VGF). Injury to the saphenous vein graft (SVG) endothelium during harvesting and implantation promotes inflammation and neointimal hyperplasia that can lead to subsequent graft atherosclerosis and occlusion. We investigated the impact of a novel intraoperative vein graft treatment SOMVC001 (DuraGraft) on angiographic early graft wall thickness and late lumen loss in patients undergoing CABG. Methods: This is a prospective randomized, double-blinded study designed to compare within patient the impact of DuraGraft vs. the standard of care by evaluating the magnitude of change in the mean wall thickness of paired grafts within patients from 4-6 weeks to 3 months and the change from 4-6 weeks to 12 months in mean lumen diameter over each graft plus the lumen diameter at maximal stenosis within-person following CABG surgery using 64-slice or better multi-detector computed tomography (MDCT) angiography. Results: Enrollment of 134 patients from seven investigational sites was completed. Patient baseline and procedural characteristics is representative of a contemporary population of individuals undergoing initial CABG with at least two SVG. MDCT angiographic follow-up is ongoing and scheduled to be completed in October 2016. Conclusions: The study data will establish whether SVGs pretreated with DuraGraft can prevent early (1 to 3 months) graft wall thickness as an expression of intima hyperplasia and late (12 months) graft lumen loss as an expression of VGF in patients undergoing CABG surgery.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".