Malignant Emboli on Transcranial Doppler During Carotid Stenting Predict Postprocedure Diffusion-Weighted Imaging Lesions
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Carotid angioplasty and stenting (CAS) has a higher incidence of periprocedural stroke compared with endarterectomy. Identifying CAS steps with the highest likelihood of embolization may have important implications. We evaluated CAS safety by correlating the findings of procedural transcranial Doppler with postprocedure diffusion-weighted imaging (DWI) lesions. METHODS: In this prospective study, transcranial Doppler monitoring was performed during CAS procedures, which were divided into 11 steps. Embolic signals on transcranial Doppler were counted and classified based on the relative energy index of microembolic signals into microemboli ≤ 1 or malignant macroemboli >1. Poststenting MRI was performed in all cases. A negative binomial regression model was used to evaluate the predictive value of transcranial Doppler emboli for new DWI lesions. RESULTS: Thirty subjects were enrolled. Seven of 30 subjects (23.3%) were asymptomatic. The median embolic signal count was 212.5 (108 microemboli and 80 malignant macroemboli). Stent deployment phase showed the highest median embolic signals count at 58, followed by protection device deployment at 30 (P=0.0006). Twenty-four of 30 (80%) had new DWI lesions on post-CAS MRI. The median DWI count was 4 (interquartile range 7). Two of 30 (6.7%) had new or worsening clinical deficits post-CAS. For every malignant embolus, the expected count of DWI lesions increases by 1% ( 95% confidence interval, 0%-2%; P=0.032). CONCLUSIONS: We observed a high incidence of embolic signals during CAS procedure, especially, when devices were deployed. Most subjects developed new DWI lesions, but only 6.7% had deficits. Malignant macroemboli predicted new DWI lesions.
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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) | 0.000 | 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) | 0.001 | 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".