One‐Year Outcome of Small‐Vessel Disease Treated with Sirolimus‐Eluting Stents: A Subgroup Analysis of the e‐SELECT Registry
Bibliographic record
Abstract
OBJECTIVES: To investigate the characteristics and one-year outcomes following sirolimus-eluting CYPHER Select Plus stent (SES) implantation in small (SmVD) and non-small vessel disease (NSmVD) in the international e-SELECT registry. BACKGROUND: Large-scale registry data are lacking on DES outcomes in SmVD treatment. METHODS: There were 4,700 SmVD (at least one vessel with estimated reference vessel diameter [RVD] < 2.5 mm, excluding 283 patients with unknown RVD vessels) and 10,139 NSmVD only patients. RESULTS: The SmVD population was older, with more women, diabetics, and vessels treated, higher mean Charlson Comorbidity Index score (CCI), shorter lesions, and less STEMI presentation. The 1-year stent thrombosis (ST) rate (primary end-point), was significantly higher (1.3% vs. 0.7%) in SmVD versus NSmVD, mainly driven by early events. One-year major adverse cardiac event (MACE), myocardial infarction (MI), and clinically indicated target-lesion revascularization (TLR) rates were significantly higher in SmVD although death and major bleeding rates were similar in both groups. Complication rates were similar between pure (3,188 patients; only RVD < 2.5 mm) and mixed (1,795 patients; some RVD < 2.5 mm or unknown RVD) SmVD. Multivariate predictors for 1-year MACE in SmVD included saphenous vein graft or bifurcation lesions, major bleeding, any antiplatelet therapy discontinuation within 1 month, age, number of stents implanted, CCI, acute coronary syndrome, and insulin-dependent diabetes mellitus. CONCLUSION: SES implantation for SmVD occurs more frequently in women, diabetics, and those with multivessel disease and comorbidities. One-year ST, MACE, MI, and clinically indicated TLR rates are higher, although low overall, in SmVD or mixed SmVD patients while death rates are similar to NSmVD.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".