Pretransplant serum phosphate levels and outcomes after kidney transplantation
Bibliographic record
Abstract
BACKGROUND: The relation between serum phosphate control while on dialysis and kidney transplant outcomes is uncertain. Our study assessed the effect of pretransplant serum phosphate levels (PTxP) on kidney transplant outcomes. METHODS: Hemodialysis patients included in the Dialysis Morbidity and Mortality Study of the US Renal Data System, undergoing kidney transplantation were studied (n=801). Delayed graft function (DGF) and graft failure as a function of PTxP, were assessed using multivariable logistic and Cox regression models. RESULTS: The within-quartile medians (interquartile range) of PTxP were 4.2 (3.7-4.5), 5.4 (5.1-5.7), 6.4 (6.1-6.8) and 8.5 (7.7-9.7) mg/dL. The adjusted odds ratio (OR) for DGF was significantly elevated for the fourth vs. first PTxP quartiles (OR=1.68; 95% confidence interval [95% CI], 1.05-2.71). Restricting the cohort to patients transplanted prior to the publication of the KDOQI bone metabolism and disease guidelines, PTxP measured within 1-year of transplant, or deceased donor recipients generally showed similar results. The adjusted hazard ratios for death-censored graft failure increased across PTxP quartiles (p for trend = 0.015). CONCLUSION: Higher PTxP is associated with an increased risk of adverse kidney allograft outcomes including DGF and death-censored graft failure. This suggests an important additional benefit of optimizing phosphate control in patients awaiting kidney transplantation while on dialysis.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".