Classification of Carotid Stenosis by Millimeter CT Angiography Measures: Effects of Prevalence and Gender
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Previous studies quantifying moderate and severe carotid stenosis by direct millimeter measures on CT angiography (CTA) did not consider how prevalence and gender may influence classification cutoff values. MATERIALS AND METHODS: Three hundred nineteen carotid arteries were evaluated in consecutive patients with known or suspected carotid artery disease. Millimeter measures were obtained of the stenotic carotid bulb lumen and distal internal carotid artery (ICA). Interclass correlation coefficients (ICC) defined interobserver and intraobserver agreement. North American Symptomatic Carotid Endarterectomy Trial (NASCET)-style percent stenosis ratios were calculated per carotid artery and used in linear regression and receiver operating characteristic (ROC) curve analysis to define equivalent millimeter quantification and classification values. Likelihood ratios and prevalence-specific positive/negative predictive values (PPV/NPV) were calculated to determine the most appropriate millimeter cutoff values to classify stenosis. RESULTS: Interobserver agreement was excellent for stenosis measures (0.90) and good for distal ICA measures (0.79). Gender-specific regression curves and ROC curves indicated that millimeter stenosis is an excellent tool to quantify and classify carotid stenosis. Assuming a 10% prevalence of severe stenosis, we found that the cutoff value maximizing NPV and PPV was 1.1 mm for both genders (female: PPV = 86.2, NPV = 97.7; male: PPV = 83.2, NPV = 95.9). Assuming a 40% prevalence of moderate stenosis, we found that the cutoff values differed between genders: female = 2.0 mm (PPV = 91.3, NPV = 91.5), male = 2.1 mm (PPV = 91.6, NPV = 92.4). Specific millimeter cutoffs will vary depending upon the clinical scenario, prevalence, and gender. CONCLUSIONS: Direct millimeter stenosis measures are an excellent tool to classify moderate and severe carotid artery stenosis. Millimeter classification cutoff values that best approximate NASCET classifications vary depending on prevalence and gender.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".