Technical recommendations for the use of carotid duplex ultrasound for the assessment of extracranial blood flow
Bibliographic record
Abstract
Duplex ultrasound is an evolving technology that allows the assessment of volumetric blood flow in the carotid and vertebral arteries during a range of interventions along the spectrum of health and chronic disease. Duplex ultrasound can provide high-resolution diameter and velocity information in real-time and is noninvasive with minimal risks or contraindications. However, this ultrasound approach is a specialized technique requiring intensive training and stringent control of multiple complex settings; results are highly operator-dependent, and analysis approaches are inconsistent. Importantly, therefore, methodological differences can invalidate comparisons between different imaging modalities and studies; such methodological errors have potential to discredit study findings completely. The task of this review is to provide the first comprehensive, user-friendly technical guideline for the application of duplex ultrasound in measuring extracranial blood flow in human research. An update on recent developments in the use of edge-detection software for offline analysis is highlighted, and suggestions for future directions in this field are provided. These recommendations are presented in an attempt to standardize measurements across research groups and, hence, ultimately to improve the accuracy and reproducibility of measuring extracranial blood flow both within subjects and between groups.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.014 |
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".