A promising tool to tackle the risk of cerebral vascular disease, the emergence of novel carotid wall imaging
Bibliographic record
Abstract
Stroke is a heterogeneous vascular disease. Carotid artery atherosclerosis is associated with almost one-quarter of ischemic strokes. Moreover, a large percentage of preventable strokes are currently attributed to carotid atherosclerosis. Over the past three decades, the management of carotid artery disease has evolved. The benefits of carotid revascularization alongside medical therapy have early been recognized. Nonetheless, the debate regarding the optimal strategy is still ongoing, particularly in patients with asymptomatic carotid artery disease. One of the challenges is the use of luminal stenosis to quantify the severity of the carotid artery disease and to guide decision-making regarding invasive revascularization. Characterizing carotid atherosclerotic plaque is a promising tool to identify vulnerable plaque. Certain features such as large lipid core have already been linked to acute vascular events, not only at the plaque level but also to predict systemic cardiovascular events. Recently, a quantitative T2 mapping magnetic resonance imaging technique was developed and validated against histology. The ability to accurately quantify plaque lipid content using this technique opens several new opportunities. In this review articles, we will discuss the current challenges in the management of carotid artery disease and the future roles of T2 mapping to aid therapeutic options. These roles may include how to determine the mode of invasive carotid revascularization in symptomatic patients. Moreover, there may be a rational to use T2 mapping as a risk stratification tool in asymptomatic patients with carotid artery stenosis. It may also provide an opportunity to stage atherosclerosis and identify patients with coronary atherosclerosis who may benefit maximally from intensive lipid interventions.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".