Rule-out and rule-in of carotid near-occlusion using color duplex ultrasound
Bibliographic record
Abstract
PURPOSE: Diagnosing carotid near-occlusion (CNO) with colour duplex ultrasound (CDU) is challenging. We hypothesised that CNO is associated with a reduced distal internal carotid artery (ICA) velocity and aimed to assess if distal velocity is able to diagnose CNO accurately. If not, we aimed to develop CDU rule-out and rule-in criteria to diagnose CNO. METHODS: This is a prospective cross-sectional study in consecutive participants with suspected ≥ 50% carotid stenosis on CT angiography (CTA). CDU velocities in the common carotid artery, the stenosis and distal to the stenosis were examined. CTAs were assessed for CNO, serving as a reference test. If no CDU parameter was both sensitive and specific for CNO, rule-out (98% sensitive) and rule-in (99% specific) criteria were created. RESULTS: Of the 315 included participants with ≥ 50% stenosis, 190 (60%) were conventional ≥ 50% stenosis and 125 (40%) CNO. No CDU parameter was both sensitive and specific for CNO. The best exclusion criteria were stenosis end diastolic velocity (EDV) ≤ 63 cm/s and/or distal peak systolic velocity (PSV) > 23 cm/s, seen in 115 (38%) participants. The best rule-in criteria were stenosis EDV ≥ 280 cm/s and/or distal PSV ≤ 23 cm/s, seen in 35 (12%) participants. Of the remaining participants, 143 (47%) were uncertain (74 CNOs) and 9 (3%) were misdiagnosed as carotid occlusion (all CNOs). CONCLUSIONS: CDU alone cannot diagnose CNO but can rule in or rule out CNO in half of participants with ≥ 50% stenosis. These criteria are intended for inclusion in an algorithm, sorting cases needing further exams, such as CTA and/or phase-contrast magnetic resonance angiography.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".