Computed tomography identified factors that preclude living kidney donation
Bibliographic record
Abstract
INTRODUCTION: The purpose of this study was to determine the variety and prevalence of renal and non-renal abnormalities detected on multidetector computed tomography (MDCT) that precluded patients from donating a kidney. METHODS: Institutional review board approval was obtained and the requirement for informed consent was waived. A retrospective, single-centre review of 701 patients (444 female, 257 male; age range 18-86 years; mean age 43.2±11.9 years) that underwent renal donor protocol MDCT was conducted. A systematic review of the CT report, records from multidisciplinary renal transplantation rounds, and electronic medical records was performed to determine which patients were approved or declined as live renal donors. If declined as a donor, CT-identified reasons were categorized as abnormalities of renal vasculature, renal parenchyma, collecting system, or extra-renal. RESULTS: A total of 81 patients were excluded as renal donors on the basis of CT findings. Abnormalities of the collecting system accounted for the most frequent cause of exclusion (n=41), with asymptomatic renal calculi being detected in 39 patients. Complex vascular anatomy and vascular abnormalities resulted in the exclusion of 29 patients. Supernumerary arteries and early arterial branching resulted in the exclusion of 20 patients, while renal vein anomalies leading to exclusion were uncommon (n=2). Abnormalities of renal parenchyma resulted in the exclusion of nine patients. Three patients were diagnosed with autosomal dominant polycystic kidney disease, two patients had renal cell carcinoma, and two patients had areas of cortical scarring. A complex cystic lesion requiring surveillance imaging was encountered in one patient and a large area of renal infarction related to prior adrenalectomy was demonstrated in one patient. Extra-renal abnormalities leading to exclusion were limited to two patients with pulmonary nodules. CONCLUSIONS: MDCT plays a critical role in the preoperative assessment of potential renal donors by identifying contraindications to donor nephrectomy and providing accurate vascular mapping. This study is anticipated to be informative for those involved in the workup of potential living renal donors by quantifying the incidence and reasons for donor exclusion identified on CT.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.005 | 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".