Effect of Contralateral Carotid Artery Stenosis on Carotid Ultrasound Velocity Measurements
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Carotid ultrasonography is being increasingly performed as the sole investigation to assess internal carotid artery (ICA) stenosis. A potential source of error in using ultrasound peak systolic velocity (PSV) measurements is that the redistribution of blood flow due to severe stenosis in a contralateral carotid artery may lead to artificially elevated values. METHODS: Ultrasonography was performed before and after carotid endarterectomy in symptomatic patients who participated in the North American Symptomatic Carotid Endarterectomy Trial (NASCET). The mean change in PSV in the unoperated artery was assessed across all degrees of angiographically defined stenosis. A simple theoretical resistance model of the cerebral circulation was also derived. RESULTS: Complete bilateral ultrasound examinations were performed within 90 days of the initial scan in 386 patients. In the presence of a contralateral severe (70% to 99%) ICA stenosis, the PSV in the unoperated artery was artificially elevated by a mean of 84 cm/s (P:=0.03; 95% CI, 10 to 159 cm/s). The mean elevation was less pronounced for lesser degrees of stenosis (11 to 21 cm/s). Small elevations (3 to 12 cm/s) were observed when the contralateral artery had <70% stenosis. The patterns of observed results were congruent with those from the theoretical model. CONCLUSIONS: The present study showed that a severely stenosed contralateral ICA can artificially elevate ultrasound PSV. Since the effect was greatest when bilaterally severe stenoses were present, caution must be exercised when assessing the degree of ICA stenosis on the basis of ultrasonography PSV measurements alone.
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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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".