Meta-Analysis of Randomized Controlled Trials Comparing the Long-Term Outcomes of Carotid Artery Stenting Versus Endarterectomy
Bibliographic record
Abstract
BACKGROUND: Stenting is an endovascular alternative to endarterectomy for the management of carotid stenosis, but its long-term safety and efficacy relative to endarterectomy remain unclear. Our objective was to compare the safety and efficacy of stenting with those of endarterectomy, with a particular focus on long-term outcomes, via meta-analysis of randomized controlled trials (RCTs). METHODS AND RESULTS: We systematically searched PubMed, EMBASE, MEDLINE, and the Cochrane Library for RCTs with ≥50 patients that compared stenting with endarterectomy in patients with carotid stenosis. Periprocedural and long-term outcomes were assessed, with data pooled across RCTs using random-effects models. Eight RCTs were included in our meta-analysis (n=7091), with follow-up ranging from 2.0 to 10.0 years. When compared with endarterectomy, stenting was associated with an increased risk of periprocedural stroke (relative risk, 1.49, 95% confidence interval [CI], 1.11 to 2.01; risk difference, 1.7%; 95% CI, 0.3 to 3.0) but a decreased risk of periprocedural myocardial infarction (relative risk, 0.47; 95% CI, 0.29 to 0.78; risk difference, -0.4%; 95% CI, -0.8% to 0.1%). During long-term follow-up, stenting was associated with an increased risk of stroke (relative risk, 1.36; 95% CI, 1.16 to 1.61) and a composite end point of ipsilateral stroke, periprocedural stroke, or periprocedural death (relative risk, 1.45; 95% CI, 1.20 to 1.75). CONCLUSIONS: Although stenting has more favorable periprocedural outcomes with respect to myocardial infarction, the observed increased risk of stroke and death throughout follow-up with stenting suggests that endarterectomy remains the treatment of choice for carotid stenosis.
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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 | 0.039 | 0.102 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.030 | 0.071 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".