The Risk of Stroke and TIA in Nonstenotic Carotid Plaques: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Severe carotid stenosis carries a high risk of stroke. However, the risk of stroke with nonstenotic carotid plaques (<50%) is increasingly recognized. PURPOSE: We aimed to summarize the risk of TIA or stroke in patients with nonstenotic carotid plaques. DATA SOURCES: We performed a comprehensive systematic review and meta-analysis in patients with acute ischemic stroke in whom carotid imaging was performed using MEDLINE and the Cochrane Database, including studies published up to December 2019. STUDY SELECTION: Included studies had >10 patients with <50% carotid plaques on any imaging technique and reported the incidence or recurrence of ischemic stroke/TIA. High-risk plaque features and the risk of progression to stenosis >50% were extracted if reported. DATA SYNTHESIS: = 122 participants) reported on the recurrence of stroke/TIA. DATA ANALYSIS: = 448 participants). LIMITATIONS: Included studies showed heterogeneity in reporting stroke etiology, the extent of stroke work-up, imaging modalities, and classification systems used for characterizing carotid stenosis. CONCLUSIONS: The risk of recurrent stroke/TIA in nonstenotic carotid plaques is not negligible, especially in the presence of high-risk plaque features. Further research is needed to better define the significance of nonstenotic carotid plaques for stroke etiology.
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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 | 0.010 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.031 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".