Carotid Anatomy Does Not Predict the Risk of New Ischaemic Brain Lesions on Diffusion-Weighted Imaging after Carotid Artery Stenting in the ICSS-MRI Substudy
Bibliographic record
Abstract
INTRODUCTION: The International Carotid Stenting Study (ICSS, ISRCTN25337470) randomized patients with recently symptomatic carotid artery stenosis > 50% to carotid artery stenting (CAS) or endarterectomy. CAS increased the risk of new brain lesions visible on diffusion-weighted magnetic resonance imaging (DWI-MRI) more than endarterectomy in the ICSS-MRI Substudy. The predictors of new post-stenting DWI lesions were assessed in these patients. METHODS: ICSS-MRI Substudy patients allocated to CAS were studied. Baseline or pre-stenting catheter angiograms were rated to determine carotid anatomy. Baseline patient demographics and the influence of plaque length, plaque morphology, internal carotid angulation, and external or common carotid atheroma were examined in negative binomial regression models. RESULTS: A total of 115 patients (70% male, average age 70.4) were included; 50.4% had at least one new DWI-MRI-positive lesion following CAS. Independent risk factors increasing the number of new lesions were a left-sided stenosis (incidence risk ratio [IRR] 1.59, 95% CI 1.04-2.44, p = .03), age (IRR 2.10 per 10-year increase in age, 95% CI 1.61-2.74, p < .01), male sex (IRR 2.83, 95% CI 1.72-4.67, p < .01), hypertension (IRR 2.04, 95% CI 1.25-3.33, p < .01) and absence of cardiac failure (IRR 6.58, 95% CI 1.23-35.07, p = .03). None of the carotid anatomical features significantly influenced the number of post-procedure lesions. CONCLUSION: Carotid anatomy seen on pre-stenting catheter angiography did not predict of the number of ischaemic brain lesions following CAS.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".