The role of enhanced stent visualization imaging in percutaneous coronary intervention: a systematic review of efficacy and clinical outcomes
Bibliographic record
Abstract
Coronary artery disease (CAD) is a major global cause of morbidity and mortality. Percutaneous coronary intervention (PCI) is central to its management, and optimal stent deployment is critical. This systematic review evaluates the efficacy and clinical outcomes associated with enhanced stent visualization (ESV) systems – x-ray-based fluoroscopic tools such as StentBoost and CLEARstent – in PCI. A systematic literature search of PubMed, PubMed Central, and Cochrane Library was conducted according to PRISMA guidelines. Inclusion criteria comprised all study types evaluating ESV use in PCI, excluding case reports and non-English articles. Study quality was assessed using Newcastle-Ottawa tool. Twelve studies involving ESV were included. ESV improved detection of stent expansion and deployment versus standard angiography and showed strong agreement with OCT and IVUS. ESV-guided PCI was associated with reduced rates of major adverse cardiac events (MACE), particularly in long-term follow-up. Radiation exposure was modestly increased but deemed acceptable. ESV systems enhance stent deployment accuracy and clinical outcomes in PCI, offering a cost-effective and accessible alternative to OCT and IVUS. Evidence supports routine integration of ESV in PCI workflows, although further large-scale trials are warranted. PROSPERO identifier is CRD420251020834.
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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 | 0.008 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".