Investigation of the effect of geometrical features of carotid artery plaque on turbulence intensity using Doppler ultrasound and particle image velocimetry
Bibliographic record
Abstract
Stenosis severity alone is not a sensitive indicator of ischemic stroke risk; however, it remains the primary indicator for clinical decision-making. Carotid endarterectomy is strongly recommended for patients with severe stenosis, while treatment for lesser stenosis severity remains disputed. Thromboembolism is one of the major causes of ischemic stroke and has shown high correlation with hemodynamic factors, such as turbulence. Geometrical factors - such as the degree of stenosis severity, plaque eccentricity, and ulceration - can alter the local hemodynamics of the carotid artery, such as by inducing flow disturbances. The objective of this work was to investigate the impact of these geometrical features on the level of turbulence intensity using DUS and PIV. A family of carotid artery models was examined with geometries ranging from disease-free to severe stenosis, in both eccentric and concentric forms of plaque symmetry, and in the case of moderate stenosis (50%) with and without ulceration. Plaque eccentricity and ulceration were found to enhance the flow disturbances downstream of a stenosis, suggesting that clinical diagnosis should consider plaque shape and roughness in addition to stenosis severity.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".