Accuracy of Duplex Ultrasound for Detecting Renal Artery Stenosis: A Systematic Review
Bibliographic record
Abstract
Background: Duplex ultrasound (DUS) is a well-tolerated, noninvasive imaging technique. Renal artery stenosis (RAS)—a narrowing of the arteries that supply the kidney—is involved in the cause of renal failure, and its gold-standard method of detection is digital subtraction angiography (DSA). Whether DUS is a suitable alternative to DSA or other angiographic modalities has only been reviewed up to 2005, producing mixed results with studies based on older ultrasound technology without a measurement of study quality. Purpose: To provide an up-to-date review of the diagnostic accuracy of DUS for the diagnosis of RAS in comparison with angiography. Methods: Our registered systematic review (DOI: 10.17605/OSF.IO/SE9VN) examined articles post-2005. Studies must have compared RAS diagnosis between DUS and angiography. Sources were searched in November 2022 and included Scopus, EMBASE, MEDLINE, CINAHL, and Academic Search Premier (1749 citations; final: n = 34; DSA only: n = 9). Study quality was assessed using the Quality Assessment of Diagnostic Studies 2 tool and results are presented narratively. A total of 2968 (1281 females) patients were included. Duplex ultrasound exhibited moderately high agreement with DSA and other well-established angiographic criteria with the existing literature having low risk of bias and low concern for patient applicability. Studies exhibiting low agreement were generally in smaller samples or used unique definitions of stenosis. Conclusions: Improved study reporting, consistent definitions of stenosis, and a common statistical battery are needed.
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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 | 0.015 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".