Chronic Kidney Disease Management: Comparison between Renal Transplant Recipients and Nontransplant Patients with Chronic Kidney Disease
Bibliographic record
Abstract
BACKGROUND/AIM: Renal transplant recipients (RTR) and patients with native chronic kidney disease (CKD) have similar complications. It is not known how the management of CKD in RTR differs from that of patients with native CKD. This study compares the management of complications related to CKD between RTR and patients with native CKD. METHODS: Cross-sectional study of all RTR with stage 4 or 5 CKD (n = 72). The control group consisted of 72 native CKD patients matched by glomerular filtration rate (within 2 ml/min/1.73 m(2)). Multivariate logistic regression analysis was performed to account for potential confounding variables. RESULTS: Multivariate analysis revealed RTR to more likely have uncontrolled hypertension (adjusted odds ratio AOR 3.8; 95% confidence interval CI 1.3-10.7), less likely to be on angiotensin-converting enzyme inhibitors (AOR 0.11; 95% CI 0.04-0.32), more likely to be anemic and not be on erythropoietin (AOR 6.4; 95% CI 0.99-41.9), and more likely to have dyslipidemia and not be on statin (AOR 4.3; 95% CI 1.4-13.4). CONCLUSIONS: This study suggests that the management of non-RTR in a multidisciplinary CKD clinic differs significantly from the CKD management in a traditional transplant clinic. A disease management approach like a multidisciplinary clinic may be an appropriate model for 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".