The Reliability of Ultrasound Measurements of Carotid Stenosis Compared to MRA and DSA
Bibliographic record
Abstract
OBJECTIVE: Carotid ultrasound (US) is a screening test for patients with transient ischemic attacks (TIAs) or stroke who then undergo Digital Subtraction Angiogram (DSA) or Magnetic Resonance Angiography (MRA). Gold standard DSA is invasive with inherent risks and costs. MRA is an evolving technology. This study compares reliability of MRA and US modes with DSA in determining degree of internal carotid artery stenosis. METHODS: A five year retrospective analysis of 140 carotid arteries from patients who had carotid US and DSA, and possibly Magnetic Resonance Angiography was undertaken. Recorded US parameters were peak systolic velocity (PSV), end diastolic velocity (EDV), and ICA/CCA peak systolic velocity ratio. The MRA and DSA parameters used NASCET technique for measuring stenosis. Statistical analysis included ROC curves and Kappa computation. RESULTS: US grading of carotid stenosis can be made more reliable by choosing appropriate parameters. The best combination of sensitivity and specificity for stenosis > 70% in our hospital was seen at PSV > 173 cm/s (sensitivity 0.87, specificity 0.8, Positive Predictive Value (PPV) 0.70, Negative Predictive Value (NPV) 0.93, kappa 0.64 and weighted kappa 0.71). MRA kappa was 0.78, (sensitivity 0.75, specificity 1.0, PPV 1.0, NPV 0.85). CONCLUSIONS: US parameters should be validated in each centre. At best, US can only approximate the accuracy of DSA, probably due to inherent limitations of this modality. Magnetic Resonance Angiography has a perfect specificity and PPV but this technique needs to be standardized. Simultaneous use of MRA and US for screening increases sensitivity to over 0.9 without compromising specificity in > 70% 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 | 0.017 | 0.079 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| 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 | 0.001 |
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".