Carotid Endarterectomy Versus Stenting: A Meta-Analysis of Randomized Trials
Bibliographic record
Abstract
BACKGROUND: A meta-analysis of randomized controlled trials (RCTs) was conducted to update the available evidence on the safety and efficacy of carotid endarterectomy (CEA) versus carotid artery stenting (CAS) in the treatment of carotid artery stenosis. METHODS: A comprehensive search was performed of MEDLINE, EMBASE, CENTRAL, bibliographies of included articles and past systematic reviews, and abstract lists of recent scientific conferences. For each reported outcome, a Mantel-Haenszel random-effects model was used to calculate odds ratios (ORs) and 95% confidence intervals (CI). The I2 statistic was used as a measure of heterogeneity. RESULTS: Twelve RCTs enrolling 6,973 patients were included in the meta-analysis. Carotid artery stenting was associated with a significantly greater odds of periprocedural stroke (OR 1.72, 95% CI 1.20 to 2.47) and a significantly lower odds of periprocedural myocardial infarction (OR 0.47, 95% CI 0.29 to 0.78) and cranial neuropathy (OR 0.08, 95% CI, 0.04 to 0.16). The odds of periprocedural death (OR 1.11, 95% CI 0.56 to 2.18), target vessel restenosis (OR 1.95, 95% CI 0.63 to 6.06), and access-related hematoma were similar following either intervention (OR 0.60, 95% CI 0.30 to 1.21). CONCLUSIONS: In comparison with CEA, CAS is associated with a greater odds of stroke and a lower odds of myocardial infarction. While the results our meta-analysis support the continued use of CEA as the standard of care in the treatment of carotid artery stenosis, CAS is a viable alternative in patients at elevated risk of cardiac complications.
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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 | 0.035 | 0.068 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.025 | 0.066 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 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".