Rapid Evaluation of Large Vessel Occlusion for Mechanical Thrombectomy Using Carotid Duplex Ultrasound
Bibliographic record
Abstract
Objectives This study clarified the usefulness of carotid duplex ultrasound (CDU) in evaluating large vessel occlusion (LVO) in patients with acute stroke planned to be treated with mechanical thrombectomy (MT). Methods This study was single-center, prospective, observational trial. If the ratio of end-diastolic velocity in the common carotid arteries was ≥1.4, or diastolic flow in the affected internal carotid artery (ICA) was absent on CDU, patients were immediately transferred to the angio-suite without additional cerebrovascular imaging. Clinical parameters, including time metrics and outcomes, were evaluated in participants. Patients We enrolled stroke patients with a National Institutes of Health Stroke Scale score ≥6 and Alberta Stroke Program Early CT score ≥6 in whom MT could be initiated within 6 hours of the stroke onset. Results Among 140 patients screened during the study period, 48 were ultimately enrolled. Twenty-seven patients were diagnosed with LVO by CDU alone. CDU offered 83% sensitivity and 82% specificity for identifying the occlusion of the ICA or M1 segment of the middle cerebral artery. Among the 29 total patients treated with MT, 20 (67%) showed a modified Rankin Scale score ≤2 at 90 days. The door-to-puncture time was significantly shorter in patients evaluated by CDU alone (34 minutes) than in those evaluated by magnetic resonance angiography after CDU (47.5 minutes, p<0.001). Conclusion CDU might reduce the time metrics for early initiation of MT with good sensitivity and specificity in identifying LVO.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".