Reliability of Carotid Doppler performed in a dedicated Stroke Prevention Clinic
Bibliographic record
Abstract
INTRODUCTION: Doppler ultrasound (DUS) is used as a screening tool to assess internal carotid artery (ICA) disease. Recent reports suggest that the DUS may be inaccurate in over 28% of patients. We sought to evaluate the accuracy of DUS, when performed in a dedicated stroke prevention clinic (SPC). METHODS: We retrospectively reviewed the charts of patients who had a DUS performed in our SPC, followed by conventional cerebral angiography. Three groups of patients were defined. Group 1 had DUS measured ICA stenosis of >50%; Group II had a DUS measured ICA stenosis of <50%; Group III had complete ICA occlusion on DUS. RESULTS: Sixty-seven patients (69 arteries) were included in the study. There were 45 patients in Group I and based on the findings of cerebral angiography, carotid endarterectomy was considered inappropriate in only one patient--a misclassification rate of 2.2% (95% CI: 0 - 6.5%). Group II consisted of 19 patients and on cerebral angiography, none of these patients had a stenosis of >50%--a misclassification rate of 0%. Group III consisted of five patients in whom DUS showed complete ICA occlusion. The angiogram confirmed the occlusion in all five patients--a misclassification rate of 0%. Overall, misclassification rate was 1.45% (95% CI: 0 - 4.3%). CONCLUSIONS: Doppler ultrasound when performed in a stroke prevention clinic (SPC), has a high accuracy in measuring ICA stenosis of >50%. Doppler ultrasound is reliable in detecting complete ICA occlusion and finally DUS is a reliable screening tool to rule out clinically significant ICA 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 | 0.008 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".