Estimated GFR for Living Kidney Donor Evaluation
Bibliographic record
Abstract
All living kidney donor candidates undergo evaluation of GFR. Guidelines recommend measured GFR (mGFR), using either an endogenous filtration marker or creatinine clearance, rather than estimated GFR (eGFR), but measurement methods are difficult, time consuming and costly. We investigated whether GFR estimated from serum creatinine (eGFRcr) with or without sequential cystatin C is sufficiently accurate to identify donor candidates with high probability that mGFR is above or below thresholds for clinical decision making. We combined the pretest probability for mGFR thresholds <60, <70, ≥80, and ≥90 mL/min per 1.73 m(2) based on demographic characteristics (from the National Health and Nutrition Examination Survey) with test performance of eGFR (categorical likelihood ratios from the Chronic Kidney Disease Epidemiology Collaboration) to compute posttest probabilities. Using data from the Scientific Registry of Transplant Recipients, 53% of recent living donors had predonation eGFRcr high enough to ensure ≥95% probability that predonation mGFR was ≥90 mL/min per 1.73 m(2) , suggesting that mGFR may not be necessary in a large proportion of donor candidates. We developed a Web-based application to compute the probability, based on eGFR, that mGFR for a donor candidate is above or below a range of thresholds useful in living donor evaluation and selection.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".