Carotid Stenosis Index Revisited With Direct CT Angiography Measurement of Carotid Arteries to Quantify Carotid Stenosis
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: All carotid stenosis ratio methods are based on the inability of digital subtraction angiography to measure in millimeters. Each method has potential flaws. The Carotid Stenosis Index (CSI) was designed to reduce ambiguities of NASCET and ECST ratios. We test this method's ability to correctly estimate carotid stenosis using direct computed tomography angiography millimeter measures of the carotid arteries. METHODS: Two neuroradiologists reviewed computed tomography angiographies of 268 carotids with atherosclerotic disease. Millimeter measurements were obtained at the narrowest diameter of the residual stenotic lumen, actual carotid bulb diameter (at level of greatest stenosis), and common carotid artery. Pearson correlation compared the CSI estimate of the carotid bulb to the actual carotid bulb measurement. Ratio calculations of the stenosis were performed using (1) CSI carotid bulb estimate and (2) actual carotid bulb measurement as denominator data. A paired-sample Wilcoxon signed rank test compared the results of these 2 ratio measurements per carotid. RESULTS: Interobserver variability was good to excellent (0.64 to 0.87). The CSI estimate of the carotid bulb size overestimated the measured carotid bulb by an average of 1.5 mm in a random distribution (correlation=0.39, N=151). Paired-sample Wilcoxon signed rank test demonstrated a significant difference between the 2 sets of ratios (z-value of -9.87, P<0.001). CONCLUSIONS: Direct measurement of carotid stenosis, vessel wall soft tissues, and computed tomography plaque imaging is now possible with the high-resolution anatomic data present in high-speed computed tomography angiography, alleviating the need for ratios and inaccurate mathematic estimations of carotid anatomy for carotid stenosis quantification.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".