P.098 Arterial wall and plaque remodeling after stent deployment in carotid stenosis: ultrasonographic study
Bibliographic record
Abstract
Background: We evaluated the effects of stents on carotid plaque and the arterial wall using carotid ultrasound in carotid stenting patients Methods: From a carotid stent database, 30 consecutive patients were selected. All had Doppler ultrasound performed pre and post-stenting. The diameters of the lumen at the level of stenotic plaque pre and post stenting, the dorsal and ventral plaque thickness, and of the outer arterial wall diameter were measured. Plaque thickness was measured at the level of maximal stenosis.Non parametric tests were used to determine whether the stent effect and luminal enlargement were based on wall remodeling or on total arterial expansion. Results: Patient was followed for an average of 22 months. 18 patients were male, average age 70 years. 87% were symptomatic ipsilateral to the side of stenosis. The luminal diameter increased post stenting in the region of severe stenosis. Plaque thickness, both ventrally, as well as dorsally decreased post stenting, with no significant difference between the ventral and dorsal plaque effects. The measured lumen in the stent increased over time post-stenting. Conclusions: Self-expanding nitinol stents alter the baseline ventral and dorsal plaque to a significant degree, and do not significantly affect the native arterial wall and the overall arterial diameter.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.003 | 0.001 |
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".