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 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 | 0.013 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.023 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".