Coronary bioresorbable stents: Non-invasive quantitative evaluation of intra- and juxta-stent plaque composition—A computed tomography longitudinal study
Bibliographic record
Abstract
PURPOSE: Coronary bioresorbable stents (BRS) do not produce blooming artifacts on computed tomography (CT), in contrast to metallic stents, as they are made of a bioresorbable polymer and are radiolucent. They allow to evaluate the coronary plaque beneath. The low-attenuation plaque (LAP) suggests plaque vulnerability and is CT assessable. The aim of our study was to show the possibility of a non-invasive CT evaluation of the volume and the LAP composition of the intra- and juxta-stent plaque. METHODOLOGY: In our prospective longitudinal study, we recruited 27 consecutive patients (35 BRS stents total; mean age 60 +/- 9 years) with bioresorbable stents for a 256-slice ECG-synchronized CT evaluation at 1- and 12-months post stent implantation. Total plaque volume (mm3), absolute and relative (%) LAP volume per block in the pre- intra- and post-stent zones were analyzed; comparison 1- and 12-months post-implantation of BRS. Changes in the previously mentioned variables were assessed by the mixed effects models with and without spline, which also accounted for the correlation between repeated measurements. RESULTS: Our block or spline model analysis has shown no significant difference in plaque or absolute LAP volumes in pre- intra- and post-stent zones between 1 and 12 months. Interestingly, % LAP volume increases near-significantly in the distal block of the intrastent at 12-mo follow-up (from 23.38 ± 1.80% to 26.90 ± 2.22% (increase of 15%), p = 0.052). CONCLUSION: Our study demonstrates the feasibility of the repeated non-invasive quantitative analysis of the intrastent coronary plaque and of the in-stent lumen by CT scan.
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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 | 0.002 |
| 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 | 0.001 | 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".