What Should the Serum Creatinine Be After Transplantation? An Approach to Integrate Donor and Recipient Information to Assess Posttransplant Kidney Function
Bibliographic record
Abstract
BACKGROUND: Knowledge of an optimal expected serum creatinine (SCr) would be useful to detect early renal dysfunction after transplantation. Current measurements of posttransplant function rely on the recipient's SCr and calculations of estimated glomerular filtration rate (eGFR), based on recipient age, weight, and sex. Renal function after transplantation, however, also depends on the donor supply of functioning nephrons and adaptation in GFR of a single kidney. METHODS: We developed a formula to predict the optimal expected SCr after transplantation derived from donor and recipient Cockroft-Gault GFRs and adjusted for the single kidney adaptive response. We compared the expected SCr with the lowest observed SCr in a cohort of living (79) and deceased (67) donor allograft recipients followed up over 5 years. RESULTS: Expected SCr correlated with the observed SCr in both living and deceased donor kidney recipients, correlation was stronger among living donor kidney recipients. Recipient-to-donor body weight ratio was significantly associated with the difference between expected and observed SCr, suggesting that recipient body weight is a major predictor of posttransplant renal function. The difference between expected and observed SCr was significantly greater among deceased donor kidney recipients, suggesting poorer function in these patients, which was not detected by SCr or estimated GFR alone. CONCLUSIONS: Calculation of expected renal function for a given donor-recipient combination adds relevant information to assessment of allograft function. Future studies will permit determination of a threshold difference between expected and observed SCr that should trigger investigation and potential intervention to improve allograft function.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".