<scp>CT</scp> volumetry is superior to nuclear renography for prediction of residual kidney function in living donors
Bibliographic record
Abstract
Living kidney donor evaluation commonly includes nuclear renography to assess split kidney function and computed tomography (CT) scan to evaluate anatomy. To streamline donor workup and minimize exposure to radioisotopes, we sought to assess the feasibility of using proportional kidney volume from CT volumetry in lieu of nuclear renography. We examined the correlation between techniques and assessed their ability to predict residual postoperative kidney function following live donor nephrectomy. In a cohort of 224 live kidney donors, we compared proportional kidney volume derived by CT volumetry with split kidney function derived from nuclear renography and found only modest correlation (left kidney R(2) =26.2%, right kidney R(2) =26.7%). In a subset of 88 live kidney donors with serum creatinine measured 6 months postoperatively, we compared observed estimated glomerular filtration rate (eGFR) at 6 months with predicted eGFR from preoperative imaging. Compared to nuclear renography, CT volumetry more closely approximated actual observed postoperative eGFR for Chronic Kidney Disease Epidemiology Collaboration (J-test: P=.02, Cox-Pesaran test: P=.01) and Mayo formulas (J-test: P=.004, Cox-Pesaran test: P<.001). These observations support the use of CT volumetry for estimation of split kidney function in healthy individuals with normal kidney function and morphology.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".