Validation and comparison of drug eluting stent to bare metal stent for restenosis rates following vertebral artery ostium stenting: A single-center real-world study
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: While drug-eluting stents (DES) have been widely applicated in coronary stenosis, uncertainty persists concerning the relative performance and clinical benefit in patients undergoing vertebral artery stenting when compared with a bare metal stent (BMS). We sought to compare in-stent restenosis (ISR) rates of DES and BMS in the treatment of vertebral artery ostium (VAO) stenosis. MATERIALS AND METHODS: This study analyzed a single-center prospective cohort. Over 1.5-year period (January 2014-June 2015), 137 consecutive patients underwent VAO stenting involving deployment of 76 DES and 74 BMS. Patient demographics, comorbidities, stenosis severity, stent diameters and lengths, periprocedural complications, imaging and duplex ultrasonography follow-up and recurrent symptoms were assessed. RESULTS: Technical success was achieved in all patients. Mean VAO stenosis at presentation were 82.4 ± 7.2% in the DES group and 83.3 ± 7.5% in the BMS group and were reduced to 12.5 ± 4.5% and 11.3 ± 4.0%. Mean stent diameter was 3.53 ± 0.40 mm in DES and 5.05 ± 0.40 mm in BMS (p < 0.0001). Mean follow-up was 12.3 months for DES and 11.7 months for BMS. The use of DES was associated with significant lower ISR rates compared with BMS (18.4% vs. 31.1%; OR = 2.628, p = 0.021). Recurrent symptoms rates were similar in DES vs. BMS (2.6% vs 2.7%, p = 0.680). Stent type and stent diameter were independent risk factors for ISR (P = 0.026). CONCLUSION: Our results suggest superior efficacy of deploying DES for the treatment of VAO stenosis with lower ISR rates as compared to BMS, but do not support significant differences in periprocedural risk and recurrent symptoms rate.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".