[PP.37.04] EVOLUTION OF ARTERIAL STIFFNESS AFTER KIDNEY TRANSPLANTATION
Bibliographic record
Abstract
Objective: Chronic kidney disease is associated with increased arterial stiffness. Correction of the uremic milieu by kidney transplantation may be improve arterial stiffness. However, the results from clinical studies are not uniformly convincing. This could be related to small sample size of studies, heterogeneity in methods and timing of assessment of arterial stiffness after transplantation. The objective of the present study is to measure the impact of renal transplantation on the reduction of arterial stiffness. Design and method: Observational studies and randomized controlled trials with measurements of arterial wave velocity (PWV) were extracted from MEDLINE, EMBASE, COCHRANE LIBRARY, and Web of Science from their inception to January 2016. Two reviewers independently identified eligible studies comparing PWV before and after kidney transplantation and extracted data including population characteristics, interventions and outcomes. Results: 11 studies with 408 subjects were available for comparing pre- to post-transplant PWV. There was a mean change of PWV by -1.28 m/s (95% CI, - 2.01 to - 0.55) post-transplantation (I2 = 72 %). When subgroup analysis was performed only for studies that had assessed aortic PWV (5 studies, 163 patients), there was a non-significant mean changes of PWV by -0.74 m/s (95% CI, -1.49 to 0.02) post-transplantation (I2 = 61%) (FIGURE). The subgroup analysis of the studies with brachial-ankle PWV (BA-PWV), which included 4 studies and 168 patients, showed a significant reduction in PWV by -2.48 m/s (95% CI, - 4.36 to - 0.60) (I2 = 84 %). Limiting analysis to the studies that have measured BA-PWV after at least 3 months of transplantation (3 studies, 151 patients) showed similar results but reduced heterogeneity (I2 = 13%).Conclusions: There is a significant reduction in overall PWV after kidney transplantation. There was a great heterogeneity in the among studies. Further analysis is required to examine the importance of changes in different vascular beds taking into account changes in blood pressure.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.411 | 0.119 |
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".