Abstract 2770: Neointimal Hyperplasia Patterns Among 3 Drug-Eluting Stents: A Comparative Intravascular Ultrasound Analysis of Everolimus-, Sirolimus-, and Paclitaxel-Eluting Stents
Bibliographic record
Abstract
Background The amount of neointimal hyperplasia as well as the percentage of neointimal stent surface coverage may be different among various types of drug-eluting stents (DES). Methods From the Stanford University Intravascular Ultrasound (IVUS) Core Laboratory database, this study consisted of the patients enrolled in prospective, multicenter clinical trials with DES deployment for de novo coronary lesions and 3-D IVUS at 8- to 9-month follow-up as part of their study protocol. In these cases, 155 single DES: (1) an 18-mm everolimus-eluting (EES, 52 stents in 50 patients); (2) an 18-mm sirolimus-eluting (SES, 51 stents); or (3) a 16-mm paclitaxel-eluting (PES, 52 stents) stent in 153 patients were investigated. Using Simpson’s rule, %neointimal volume was defined as neointimal volume / stent volume × 100. Circumferential stent length covered with neointima (L N ) and stent perimeter (L S ) were also measured at every 1-mm cross section throughout the stent. Then, %neointimal coverage was defined as total L N / total L S × 100. Results EES had comparable %neointimal volume to SES, but less %neointimal volume than PES (5.2±5.3 vs 2.6±4.0 vs 9.2±8.7%, P< 0.0001), whereas EES had greater %neointimal coverage than SES, but comparable %neointimal coverage to PES (25.7±19.9 vs 9.1±14.6 vs 29.3±23.5%, P< 0.0001). Conclusions Compared to SES, EES had comparable neointimal hyperplasia, but greater neointimal stent coverage by IVUS. Compared to PES, EES had less neointimal hyperplasia, but comparable neointimal stent coverage by IVUS. This unique pattern of neointimal hyperplasia with these platforms may be important to the balance of the short-term efficacy and the long-term safety.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".