Uric Acid and the Risk of Graft Failure in Kidney Transplant Recipients: A Re-Assessment
Bibliographic record
Abstract
The association of hyperuricemia with kidney allograft outcomes remains controversial. We studied this problem in 1170 kidney transplants from January 2000 to December 2010. The primary endpoint was total graft failure (i.e. graft loss or death). Conventional, time-dependent and marginal structural Cox proportional hazards models were fitted, the latter accounting for kidney function as a time-varying confounder affected by prior uric acid levels. Uric acid level was associated with an increased risk of total graft failure in time-fixed and time-varying models (HR 1.02 [95% CI: 1.003-1.04] and HR 1.02 [95% CI: 1.01-1.03], respectively, for every 10 µmol/L increase in uric acid). In contrast, the marginal structural model showed a modestly protective effect (HR 0.90 [95% CI: 0.85-0.94] for every 10 µmol/L increase in uric acid). Similar results were observed for death-censored graft failure and death with graft function. In summary, the absence of a deleterious association between elevated uric acid and graft outcome after accounting for graft function as a time-varying confounder suggests that uric acid is not an independent risk factor for graft failure. The modestly protective effect of uric acid may be an indicator of nutritional status but further study is warranted.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".