Evaluating potential live-renal donors: Causes for rejection, deferral and planned procedure type, a single-centre experience
Bibliographic record
Abstract
BACKGROUND: Renal transplantation is the preferred therapy to extend life expectancy and quality of life for patients with chronic kidney disease. There are many barriers in the process of live kidney donation that prevent the timely progression from organ requirement to transplantation, including the progression of the live donor through a medical evaluation. We assess how easily patients complete the donor workup, how often the medical evaluation identifies significant incidental findings, and which surgical procedure is planned for organ retrieval. METHODS: We reviewed our donor database and the minutes from our multidisciplinary rounds from 2002 to 2008 to assess how medical, radiological and psychological findings were used to decide on the candidacy of potential donors. RESULTS: Half (50.2%) of patients did not pass the initial health screen. Of the 467 patients who progressed beyond the health screen to the computed tomographic angiogram evaluation, 48 (10.3%) were excluded as donors and 419 (89.7%) were accepted. Of those accepted, 136 (32.5%) were conditional on further medical workup. Of the patients accepted (n=419), 375 (89.5%) were planned for laparoscopic left-sided approach. CONCLUSIONS: The vast majority of patients who passed the initial health screen for kidney donation will be accepted as donors, but about one-third will require further workup. It is rare to identify life-threatening disease on screening computerized tomographic angiograph for kidney donor workup.
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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 | 0.001 | 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 | 0.002 | 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) | 0.005 | 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".