Initiating Maintenance Dialysis Before Living Kidney Donor Transplantation When a Donor Candidate Evaluation Is Well Underway
Bibliographic record
Abstract
BACKGROUND: Preemptive kidney transplants result in better outcomes and patient experiences than transplantation after dialysis onset. It is unknown how often a person initiates maintenance dialysis before living kidney donor transplantation when their donor candidate evaluation is well underway. METHODS: Using healthcare databases, we retrospectively studied 478 living donor kidney transplants from 2004 to 2014 across 5 transplant centers in Ontario, Canada, where the recipients were not receiving dialysis when their donor's evaluation was well underway. We also explored some factors associated with a higher likelihood of dialysis initiation before transplant. RESULTS: A total of 167 (35%) of 478 persons with kidney failure initiated dialysis in a median of 9.7 months (25th-75th percentile, 5.4-18.7 months) after their donor candidate began their evaluation and received dialysis for a median of 8.8 months (3.6-16.9 months) before kidney transplantation. The total cohort's dialysis cost was CAD $8.1 million, and 44 (26%) of 167 recipients initiated their dialysis urgently in hospital. The median total donor evaluation time (time from evaluation start to donation) was 10.6 months (6.4-21.6 months) for preemptive transplants and 22.4 months (13.1-38.7 months) for donors whose recipients started dialysis before transplant. Recipients were more likely to start dialysis if their donor was female, nonwhite, lived in a lower-income neighborhood, and if the transplant center received the recipient referral later. CONCLUSION: One third of persons initiated dialysis before receiving their living kidney donor transplant, despite their donor's evaluation being well underway. Future studies should consider whether some of these events can be prevented by addressing inappropriate delays to improve patient outcomes and reduce healthcare costs.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".