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Record W4413889933 · doi:10.1055/a-2685-3141

Carotid Revascularization in the Modern Era: A Comparative Review of Carotid Endarterectomy, Carotid Angioplasty and Stenting, and Transcarotid Artery Revascularization

2025· review· en· W4413889933 on OpenAlexaff
Abdelaziz Amllay, Andrew B. Koo, Daniela Renedo, Varun Padmanaban, Ben Teasdale, Ryan Hebert, Anıl Arat, Taylor Duda, Joseph Schindler, Christopher J. Stapleton, James D. Rabinov, Aman B. Patel, Charles Matouk, Nanthiya Sujijantarat

Bibliographic record

VenueSeminars in Neurology · 2025
Typereview
Languageen
FieldMedicine
TopicCerebrovascular and Carotid Artery Diseases
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicineCarotid endarterectomyAngioplastyRevascularizationStenosisStroke (engine)EndarterectomyCarotid stentingAsymptomaticCarotid arteriesCardiologyRadiologyInternal medicineSurgery

Abstract

fetched live from OpenAlex

Carotid artery stenosis is a major cause of acute ischemic stroke, accounting for approximately 15% of cases. Although optimal medical therapy remains the cornerstone of management, current guidelines recommend consideration of surgical intervention for symptomatic patients with ≥50% stenosis and asymptomatic patients with ≥70% stenosis. Extensive evidence supports carotid endarterectomy (CEA) as the gold standard procedure, whereas transfemoral carotid angioplasty and stenting (TF-CAS) and transcarotid artery revascularization (TCAR) offer safe alternatives for patients with high surgical risk. Emerging data suggest that TCAR provides safety and efficacy profiles comparable to CEA and superior to TF-CAS in select patients. Considering these findings, selecting an appropriate revascularization strategy should rely on a multidisciplinary, individualized risk-benefit assessment. This article aims to provide a comparative review of the latest evidence on clinical indications, surgical techniques, and outcomes for current carotid revascularization strategies.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.162
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.018
GPT teacher head0.297
Teacher spread0.279 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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