Reduction of Arterial Stiffness After Kidney Transplantation: A Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
Background End‐stage kidney disease is associated with increased arterial stiffness. Although correction of uremia by kidney transplantation ( KT x) could improve arterial stiffness, results from clinical studies are unclear partly due to small sample sizes. Method and Results We conducted a systematic review and meta‐analysis of before‐after design studies performed in adult KT x patients with available measures of arterial stiffness parameters (pulse wave velocity [ PWV ], central pulse pressure [PP], and augmentation index) before and at any time post‐ KT x. Mean difference of post‐ and pre‐ KT x values of different outcomes were estimated using a random effect model with 95% confidence interval. To deal with repetition of measurement within a study, only 1 period of measurement was considered per study by analysis. Twelve studies were included in meta‐analysis, where a significant decrease of overall PWV by 1.20 m/s (95% CI 0.67‐1.73, I 2 =72%), central PWV by 1.20 m/s (95% CI 0.16‐2.25, I 2 =83%), peripheral PWV by 1.17 m/s (95% CI 0.17‐2.17, I 2 =79%), and brachial‐ankle PWV by 1.21 m/s (95% CI 0.66‐1.75, I 2 =0%) was observed. Central PP (reported in 4 studies) decreased by 4.75 mm Hg (95% CI 0.78–10.28, I 2 =50%). Augmentation index (reported in 7 studies) decreased by 10.5% (95% CI 6.9‐14.1, I 2 =64%). A meta‐regression analysis showed that the timing of assessment post‐ KT x was the major source of the residual variance. Conclusions This meta‐analysis suggests a reduction of the overall arterial stiffness in patients with end‐stage kidney disease after KT x.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.024 |
| 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) | 0.004 | 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".