The Diagnostic Accuracy of Sonographic Parameters for Renal Artery Stenosis in Adults: A Rapid Literature Review Based on a Statistical Approach
Bibliographic record
Abstract
Objective: The aim of this study was to determine the 95% confidence interval (CI) cutoff for sonographic renal artery stenosis (RAS) parameters. A secondary objective was to determine the diagnostic accuracy parameters of peak systolic velocity (PSV), renal aortic ratio (RAR), acceleration index (AI), and acceleration time (AT) for diagnosing RAS. Materials and Methods: Diagnostic test accuracy (DTA) parameters and 95% CIs were evaluated for the sonographic cutoff values. A total of 31 articles were extracted and subjected to statistical analysis. Results: The mean 95% CI cutoff for PSV, RAR, AI, and AT were 192.50 (175.16, 209.84), 3.10 (2.83, 3.38), 3.39 (2.51, 4.27), and 80.78 (68.56, 93.01), respectively. The pooled mean 95% CI sensitivity of PSV, RAR, AI, and AT were 85.90% (79.84, 91.97), 82.34% (77.58, 87.11), 74.92% (64.53, 85.33), and 73.57% (63.01, 84.13), respectively, whereas the pooled mean specificity of the same parameters was 82.52% (75.78, 89.25), 86.97% (83.12, 90.82), 78.93% (66.34, 91.52), and 82.57% (70.70, 94.44), respectively. Conclusion: The pooled mean 95% CI for sensitivity and specificity of the sonographic parameters was concordant with the literature. Based on this higher level of evidence, except RAR, the mean 95% CI cutoff for PSV, AI, and AT were consistent with the cutoff values encountered in the published literature.
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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 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.049 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".