Comparison of Doppler Ultrasound and Digital Subtraction Angiography in extracranial stenosis
Bibliographic record
Abstract
OBJECTIVE: Evaluating the degree of extracranial stenosis is important in predicting the risk of cerebrovascular events and to assess if the patient can benefit from any intervention. Non-invasive methods, like Doppler Ultrasonography (DUS) are preferred to invasive methods such as Digital Subtraction Angiography (DSA). METHODS: In this retrospective study, the level of agreement between DUS and DSA regarding the degree of stenosis of Internal Carotid Arteries (ICAs) and Vertebral Arteries (VAs) was assessed. The degree of ICA stenosis was classified into 5 groups. DSA was assumed as the gold standard. VA stenosis was classified into two groups of more or less than 50% stenosis. RESULTS: A total of 428 ICAs were assessed. Based on DSA results, DUS could estimate the degree of arterial stenosis in groups of 0-15% stenosis and 100% stenosis most accurately, and the least accuracy was in groups of 50-69% and 70-99% stenosis. The overall agreement between DUS and DSA in the classified ICA stenosis was moderate (Weighted Kappa = 0.565, P < 0.001). Also, the agreement of DUS and DSA when classifying ICA stenosis into two groups of above and below 50%, was moderate (Kappa = 0.583, P < 0.001). DUS was most sensitive and specific in the group of 100% stenosis (Sensitivity: 0.75 Specificity: 0.99) as well as the group of 1-15% stenosis (Sensitivity: 0.80 Specificity: 0.76). Also, DUS was least sensitive in group of 50-69% stenosis (Sensitivity: 0.11 Specificity: 0.94). Regarding VAS, 108 arteries were assessed and the agreement between DUS and DSA was fair (Kappa = 0.248, CI95 = -0.013 - 0.509, P < 0.01). CONCLUSIONS: DUS can be used as the first-line screening tool for detecting extra cranial arteries stenosis. The practicality of the DUS as a screening tool for extracranial VAs stenosis appears to be limited.
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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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".