Can the outcome of older donor kidneys in transplantation be predicted? An analysis of existing scoring systems
Bibliographic record
Abstract
The use of older cadaveric donors in kidney transplantation is increasing. The transplant outcome of the single older kidney is generally inferior prompting some to recommend dual kidney transplantation. The ability to predict the outcome of the solitary marginal kidney becomes clinically important. Such insight might allow for better allocation strategies that would minimize poorer outcomes while permitting optimal rationalization of this scarce resource. A retrospective, single center review of 79 single kidney transplants from 50 donors aged > or =55 yr was performed. We tested the validity of published scoring strategies to predict subsequent recipient kidney function. Receiver operating characteristic curve analysis was used to quantify the donor strategies separating good and poor outcomes based upon recipient creatinine clearance (CrCl) <30 mL/min. Two pre-transplant donor assessment strategies, Nyberg score and donor CrCl (dCrCl) were found to predict subsequent kidney function in recipients. When Nyberg variables (cold ischemia time, donor diabetes and hypertension status, incremental donor age >55 yr and cause of death) in conjunction with the dCrCl were considered, they were no better than dCrCl alone. Although dCrCl had a reasonable negative predictive ability, the positive predictive value was <50%. Our analysis suggests that a dCrCl of > or =70 mL/min is a better discriminator of subsequent kidney function outcomes than a dCrCl of 90 mL/min as recommended by the Dual Transplant Registry.
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 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 | 0.008 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".