The impact of measuring split kidney function on post-donation kidney function: A retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: Studies have reported agreement between computed tomography (CT) and renography for the determination of split kidney function. However, their correlation with post-donation kidney function remains unclear. We compared CT measurements with renography in assessment of split kidney function (SKF) and their correlations with post-donation kidney function. METHODS: A single-centre, retrospective cohort study of 248 donors from January 1, 2009-July 31, 2019 were assessed. Pearson correlations were used to assess post-donation kidney function with renography and CT-based measurements. Furthermore, we examined high risk groups with SKF difference greater than 10% on renography and donors with post-donation eGFR less than 60 mL/min/1.73m2. RESULTS: 62% of donors were women with a mean (standard deviation) pre-donation eGFR 99 (20) and post-donation eGFR 67 (22) mL/min/1.73m2 at 31 months of follow-up. Post-donation kidney function was poorly correlated with both CT-based measurements and renography, including the subgroup of donors with post-donation eGFR less than 60 mL/min/1.73m2 (r less than 0.4 for all). There was agreement between CT-based measurements and renography for SKF determination (Bland-Altman agreement [bias, 95% limits of agreement] for renography vs: CT volume, 0.76%, -7.60-9.15%; modified ellipsoid,1.01%, -8.38-10.42%; CC dimension, 0.44%, -7.06-7.94); however, CT missed SKF greater than 10% found by renography in 20 out 26 (77%) of donors. CONCLUSIONS: In a single centre study of 248 living donors, we found no correlation between CT or renography and post-donation eGFR. Further research is needed to determine optimal ways to predict remaining kidney function after donation.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| 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".