A Review on the Comparison of Different Treatments for Carotid In-Stent Restenosis
Bibliographic record
Abstract
Different treatment options for carotid in-stent restenosis (ISR) have been reported with good outcome, including carotid endarterectomy (CEA), repeated carotid angioplasty stenting (CAS) and percutaneous transluminal angioplasty (PTA) with drug-coated balloons (DCBs). However, the optimal treatment option for ISR has not yet been determined. A systematic literature search was performed in the databases of Medline, Embase, Cochrane library, and unpublished data from clinicaltrials.gov from 1990 to March 1, 2019. Studies were enrolled if they reported treatment strategies for carotid ISR treatment and met the inclusion criteria. After study inclusions, data were extracted and summarized. Totally 25 cross-sectional studies were included, containing 5 comparative studies, 16 studies using repeated PTA, and 4 studies adopting CEA treatment. Our study summarized the current available data, showing that all the studies could effectively relieve the carotid ISR by significantly improving the angiographic stenosis and decreasing the peak systolic velocity values. Meanwhile, CEA treatment had the best long-term effects in relieving restenosis, while re-PTA with stenting/balloon angioplasty had a certain rate of restenosis, ranging from 33% to 83%. Furthermore, re-PTA/stenting and balloon angioplasty treatment had less complications compared with CEA. Also, we analyzed the risk factors that might affect the long-term prognosis of carotid ISR patients. The therapeutic measures for carotid ISR had their own features, with CEA had the highest efficacy while re-PTA/stenting and balloon angioplasty were with less complications. More large-scale comparative clinical studies are needed to further ascertain the best strategies.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".