Effect of Time on Dialysis and Renal Transplantation on Endothelial Function
Bibliographic record
Abstract
BACKGROUND: Soluble vascular cell adhesion molecule-1 (sVCAM-1) is a marker of endothelial injury and a potent predictor of cardiovascular mortality in patients with kidney failure on dialysis. The longitudinal effects of dialysis on endothelial dysfunction and in particular the effects of renal transplantation on markers of endothelial function including sVCAM-1 have not been well characterized. METHODS: We used the Transplant Manitoba registry and biobank to assemble a retrospective cohort of all patients receiving a first kidney transplant between January 1, 2000, and December 31, 2005 (n=186). One hundred seventy-four patients had at least two serum samples pretransplant and at least two samples posttransplant. In total, 1,004 serial samples (median 5/patient) were analyzed. Factors associated with sVCAM-1 were examined using mixed linear models. RESULTS: The sVCAM-1 levels increased progressively on dialysis (0.15 [0.10 to 0.20] ng/mL/day; P<0.0001), fell significantly within 1 month after transplantation (-625 ng/mL/day; P<0.0001) and continued to fall thereafter (-0.23 [-0.34 to -0.12] ng/mL/day). Smoking and heart failure were associated with higher sVCAM-1 levels, whereas transplantation was associated with lower sVCAM-1 levels. The relationship between sVCAM-1 and transplantation was not changed by multivariate adjustment. CONCLUSION: Endothelial injury worsens over time on dialysis but improves significantly after renal transplantation.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".