Symptomatic non-stenotic carotid disease: current challenges and opportunities for diagnosis and treatment
Bibliographic record
Abstract
Symptomatic non-stenotic carotid plaques (SyNC) are an under-researched and under-recognized source of stroke. Various imaging markers of non-stenotic carotid plaques that are associated with stroke risk have been identified, but these causal relationships need to be confirmed in additional prospective studies. Currently, there exists neither a standardized SyNC definition nor a dedicated set of imaging protocols, although researchers have started to address these shortcomings. Moreover, many neuroradiologists are still unaware of the condition, and hence do not comment on high-risk plaque features other than stenosis in their reports. Regarding SyNC treatment, scant data exist as to whether and to what extent medical, interventional and surgical treatments could influence the course of the disease; the relative lack of data on the 'natural' history of untreated SyNC makes treatment comparisons difficult. In our opinion, endovascular SyNC treatment represents the most promising treatment option for SyNC, since it allows for targeted elimination of the embolic source, with few systemic side effects and without the need for general anesthesia. However, currently available carotid devices are designed to treat stenotic lesions, and thus are not optimally designed for SyNC. Developing a device specifically tailored to SyNC could be an important step towards establishing endovascular SyNC treatment in clinical practice. In this review, we provide an overview of the current state of evidence with regard to epidemiological, clinical and imaging features of SyNC, propose a SyNC definition based on imaging and clinical features, and outline a possible pathway towards evidence-based SyNC therapies, with a special focus on endovascular SyNC treatment.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".