Carotid plaque vulnerability on magnetic resonance imaging and risk of future ischemic events: a systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: Magnetic resonance imaging (MRI) can characterize carotid plaque features, including intraplaque hemorrhage (IPH), lipid-rich necrotic core (LRNC), and thin/ruptured fibrous cap (TRFC), that have increased tendency to cause future cerebrovascular ischemic events. We performed a systematic review and meta-analysis of studies evaluating association of MRI-identified high-risk plaque features, including IPH, LRNC, and TRFC, with risks of subsequent ischemic events of stroke, transient ischemic attack (TIA), or amaurosis fugax (AF) over follow-up duration of ≥3 months. EVIDENCE ACQUISITION: Multiple databases were searched for relevant publications between January 2000 and March 2020. Studies reporting outcomes of future ischemic events of stroke, TIA, or AF for individual MRI-identified high-risk carotid plaque features over follow-up duration of ≥3 months were included. Random effects meta-analysis was performed to estimate odds ratios (OR) and 95% confidence intervals (CI) comparing outcomes between MRI-positive and MRI-negative groups. EVIDENCE SYNTHESIS: Fifteen studies including 2350 patients were included. The annual rate of future ischemic events was 11.9% for MRI-positive IPH, 5.4% for LRNC, and 5.7% for TRFC. IPH, LRNC, and TRFC were associated with increased risk of future ischemic events (OR 6.37; 95% CI, 3.96 to 10.24), (OR 4.34; 95% CI, 1.65 to 11.42), and (OR 10.60, 95% CI 3.56 to 31.58), respectively. CONCLUSIONS: The current study findings strengthen the assertion that MRI-positive "high-risk" or "vulnerable" plaque features, including IPH, LRNC, and/or TRFC can predict risks of future ischemic events of stroke, TIA, or AF.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".