The Flow of Living Kidney Donor Candidates Through the Evaluation Process: A Single-Center Experience in Ontario, Canada
Bibliographic record
Abstract
Introduction: Tracking the evaluation process of living kidney donor candidates facilitates benchmarking and can inform process redesign to improve experiences with the evaluation and enable more living donor kidney transplantation. Methods: We reviewed the medical records for all living donor candidates who were actively undergoing evaluation at any time between January 1, 2013, and December 31, 2016, at the London Health Sciences Centre in London, Ontario, Canada. We abstracted information on demographic factors, the evaluation process, reasons for a delayed evaluation, reasons for an evaluation termination (eg, donation, decline, withdrawal, loss to follow-up), frequency and timing of evaluation testing, and recipient dialysis status. Results: Over time, the number of living donor kidney transplants increased from 22 in 2013 to 32 in 2016 (18% and 34% of which were pre-emptive, respectively). The median number of candidates coming forward doubled from 167 in 2013 (2 candidates per recipient) to 348 in 2016 (4 candidates per recipient). Median time from first contact until donation decreased from 12.8 months in 2013 to 7.1 months in 2016 (a 45% reduction). The time from computed tomography (CT) angiography until donation (n = 74) was a median of 75 (interquartile range [IQR] = 36, 180) days, the longest single step in the evaluation. Common reasons for delay included waiting for the referral of their intended recipient for transplant evaluation (11% of candidates) and a need for the donor candidate to lose weight (8% of candidates). Donors completed the main evaluation tests on a median of 5 different dates. Thirty-six recipients started dialysis after their living donor candidates' evaluation had been underway for at least 3 months. Conclusion: Tracking the steps and reasons for an inefficient living kidney donor evaluation process can be used for quality improvement, and efficiency improvements are expected to translate into improved outcomes and experiences.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".