The Efficacy of Transcarotid Artery Revascularization With Flow Reversal System Compared to Carotid Endarterectomy: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Carotid artery stenosis has long been a critical cause of stroke and death, and it can seriously affect the life quality. Transcarotid artery revascularization (TCAR) and carotid endarterectomy (CEA) are both feasible therapies for this disease. This systematic review and meta-analysis aim to evaluate if the efficacy of the two approaches is comparable. Methods: Clinical studies up to March 2021 were searched through PubMed, Embase, and Scopus from a computer. The screening process was designed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement. Newcastle-Ottawa Scale (NOS) was used for methodological quality assessment of works of literature meeting the inclusion criteria, and Review Manager 5.4 was used for data synthesis. The I2 statistic was performed to measure the heterogeneity, and M-H/I-V fixed or random model was utilized depending on the I2 value. The evidence evaluation was accomplished based on grades of recommendation, assessment, development, and evaluation (GRADE) online tool. Results: A total of 14,200 subjects (six comparative studies) were finally included in this pooled study. There is no statistical discrepancy between the two treatments on reducing stroke/death/myocardial infarction (odds ratio [OR] 0.85, 95% CI 0.67–1.07), stroke (OR 1.03, 95% CI 0.77–1.37), or death (OR 1.14, 95% CI 0.67–1.94). Besides, TCAR is associated with a lower incidence of myocardial infarction (P = 0.004), cranial nerve injury (P < 0.00001), and shorter procedure time (P < 0.00001) than CEA among the overall cohort. Conclusions: TCAR is a rapidly developing treatment that reaches a comparable prognosis to CEA and significantly reduces the risk of myocardial infarction under the well-matched condition, which is a dependable choice for patients with 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 | 0.018 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.026 | 0.053 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".