Evaluation of renal parenchyma stiffness in children with nephropathy by shear wave elastography: A systematic review and <scp>meta‐analysis</scp>
Bibliographic record
Abstract
BACKGROUND: The role of shear wave elastography (SWE) in assessing renal parenchymal stiffness in children with nephropathy is obscure. This systematic review and meta-analysis investigated this issue. MATERIALS AND METHODS: PubMed, Embase, Web of Science, and the Cochrane Library databases were searched for studies evaluating renal parenchyma stiffness in children with nephropathy by SWE from inception to October 2021. The search was not limited by language. Two investigators independently screened the literature and extracted data. Any discrepancies were resolved via discussion with the senior professor. Study quality was assessed by the Newcastle-Ottawa Scale and the standardized mean difference of shear wave velocity (SWV) for the evaluation of renal parenchyma stiffness was determined. RESULTS: Eight studies involving a total of 496 children with nephropathy and 353 healthy children were selected. Eight studies used SWV as parameters of renal parenchyma stiffness. The SWV was not significantly different in children with renal lesion than in those without renal lesion, with a standardized mean difference of 0.49 (95% confidence level, -0.40 to 1.39, p = 0.28). There was a high heterogeneity between studies. CONCLUSION: Although there was significant difference in SWE of renal parenchyma between controls and patients in each study we included, statistical differences were not seen after results of all research were amalgamated due to different diseases with different pathomechanisms. SWE could be used to evaluate renal parenchymal stiffness in children with kidney disease after more well-designed and high-quality studies with a large sample size will be performed in the future.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.022 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".