Shear Wave Elastography in the Evaluation of Renal Parenchymal Stiffness in Patients With Chronic Kidney Disease: A Meta-Analysis
Bibliographic record
Abstract
Background: Chronic kidney disease (CKD) is a progressive loss of renal function, mainly due to hypertension, diabetes, and primary kidney disease. The histopathological findings are that of glomerulosclerosis, tubulointerstitial fibrosis and loss of renal parenchyma characterized. Therefrom, CKD can lead to higher morbidity and mortality. Patients with CKD have multiple risk factors, and the prevention work is complicated and arduous. Therefore, it is important to quantify the severity of CKD. The aim of this study is to investigate the value of shear wave elastography (SWE) in the evaluating renal parenchymal stiffness in patients with CKD. Methods: We carried out the systematic search of databases for publications in PubMed, Embase and Cochrane Library on SWE evaluating renal fibrosis in patients with CKD. The Endnote X9, STATA 16, Review Manager 5.3 and other software were used to sort out documents, extract, integrate and analyze data. Results: The outcomes were utilized to appraise the diagnostic accuracy of SWE and diagnose the CKD with renal fibrosis. A total of 405 patients were enrolled and their data analysis results were as follows: summary of sensitivity (S-SEN) 84% (95% confidence interval (CI): 80-87%); specificity (S-SPE) 80% (95% CI: 76-84%); summary of DLR (digital light radiography) positive (DLR-P) 4.29 (95% CI: 3.43 - 5.37); and DLR negative (DLR-N) 0.20 (95% CI: 0.16 - 0.25). The corresponding areas under the curves of diagnostic odds ratio (DOR) and summary receiver operating characteristic curve (SROC) were 21.50 (95% CI: 14.69 - 31.46) and 89% (95% CI: 86-92%), respectively. Conclusions: SWE is highly accurate and has clinical significance for evaluating renal fibrosis, especially when the shear modulus value is used as the threshold.
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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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.033 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".