Comparison of Carotid Doppler Ultrasound to Other Angiographic Modalities in the Measurement of Carotid Artery Stenosis
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The purpose of this study was to compare Doppler ultrasound (DUS) to other angiographic modalities: computed tomography angiography (CTA), magnetic resonance angiography (MRA), and digital subtraction angiography (DSA). METHODS: All DUS studies performed at Stroke Prevention Clinic (SPC) from 2011 to 2013 and referred for further angiographic modalities were included. Patients were excluded if the corresponding angiographic modality was not performed within 6 months of DUS. Patients were also excluded if they underwent interventions before DUS or between the time of DUS and the corresponding angiographic modality. The degree of stenosis was classified as mild (<50%), moderate (50-69%), severe (70-99%), or occlusion (100%). RESULTS: In total, 245 patients were identified. Nine patients were excluded (3.7%). Overall 472 Doppler studies of single ICAs from 236 patients were included in our analysis. Age was 65 ± 13 years and 136 patients were males (57.6%). There was an excellent agreement between DUS and CTA (kappa = .9 [P < .001], n = 274), good agreement with MRA (kappa = .8 [P < .001], n = 242), and excellent agreement with DSA (kappa = .92 [P < .001], n = 18). There was excellent agreement between CTA and MRA (kappa = .87, n = 46). CONCLUSION: Doppler ultrasound performed in a dedicated SPC by an experienced sonographer and reviewed by a certified stroke neurologist serves as a reliable initial screening tool in determining carotid artery stenosis.
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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 | 0.012 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".