Drug-coated balloons for the treatment of in-stent restenosis: an updated systematic review and meta-analysis of randomized controlled trials
Bibliographic record
Abstract
Background: Drug-coated balloons (DCBs) have emerged as a potential option for treating in-stent restenosis (ISR). We aimed to assess the efficacy and safety of DCB compared to uncoated balloons (UCBs) for patients with ISR. Methods: We systematically searched Embase, MEDLINE, and Cochrane Library for randomized controlled trials comparing drug-coated balloons with uncoated balloons for coronary in-stent restenosis (ISR). Dichotomous and continuous outcomes were pooled using Revman 5.4 under a random-effects model. All the outcomes were assessed in a 6-month to 1-year follow-up period. Results: A total of seven RCTs involving 1407 patients were included in our review. DCBs significantly reduced the rate of target lesion revascularization (TLR) (RR 0.28; 95% CI: 0.16, 0.48), target vessel revascularization (TVR) (RR 0.36; 95% CI: 0.14, 0.90), major adverse cardiovascular events (MACE) (RR 0.39; 95% CI: 0.26, 0.58), and MI (RR 0.56; 95% CI: 0.35, 0.91). However, no significant difference was observed between the two groups regarding all-cause mortality, cardiovascular mortality, and stent thrombosis. DCB significantly improved angiographic outcomes such as binary restenosis (RR 0.23; 95% CI: 0.12, 0.43), late lumen loss (MD -0.44; 95% CI: - 0.55 to -0.33), and percent diameter stenosis (MD -13.91; 95% CI: - 24.15 to -3.67) compared to the UCB group. Conclusion: Our meta-analysis showed that drug-coated balloons have superior efficacy and safety compared to uncoated balloons in coronary in-stent restenosis patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.015 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".