Elucidating the hemodynamic impact of residual stenosis post‐carotid artery stenting: A numerical study
Bibliographic record
Abstract
BACKGROUND: Residual stenosis (RS) and hemodynamics demonstrate a significant correlation with postoperative in-stent restenosis/thrombosis following carotid artery stenting (CAS). PURPOSE: This study endeavors to elucidate the potential associations between RS and adverse postoperative hemodynamic factors. METHODS: This study utilized 46 patient-specific carotid artery models post-stenting, which were categorized into two groups based on the presence of RS: the normal group (N, n = 23) and the RS group (RS, n = 23). A comparative analysis was conducted to evaluate the discrepancies in geometry and adverse hemodynamic parameters, alongside investigating the potential correlation between hemodynamic and geometric parameters. RESULTS: The results reveal that a higher reflux flow volume is discernible in the RS group during low-velocity phases of the cardiac cycle, concomitant with an augmented extent of areas exposed to oscillatory shear stress and extended particle residence time. Moreover, the adverse hemodynamic parameters exhibit a positive correlation with the degree of stent expansion, stent length in the common carotid artery (CCA), and the distal slope of the RS. CONCLUSION: The distal slope and tortuosity of RS significantly influence the development of adverse hemodynamic conditions post-stenting, exacerbating the hemodynamic environment near the stenosis. Interestingly, while an extended stent length in the internal carotid artery (ICA) region improves hemodynamics by reducing flow disturbance, a longer stent in the CCA significantly worsens these conditions. Hence, it is prudent to analyze the characteristics of the local lesion regions to optimize the strategy for stent implantation.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".