Potential implications of a more timely living kidney donor evaluation
Bibliographic record
Abstract
Living donor kidney transplantation is the most promising way to avoid or minimize the amount of time a recipient spends on dialysis before transplantation. We studied 887 living kidney donors at 5 transplant centers in Ontario, Canada, who started their evaluation and donated between April 2006 and March 2014. Using a series of hypothetical scenarios, we estimated the impact of an earlier living donor evaluation completion and donation on the number pre-emptive transplants, the time spent on dialysis, healthcare cost savings from averted dialysis costs (CAD $2016), and the number of additional transplants. During the study period, if the donor transplants occurred 3 months earlier, the healthcare system would save on average $12 055 (standard deviation [SD] $13 594) per recipient; 21 recipients could have avoided dialysis altogether, and 57 additional transplants (a 26% increase) could have occurred each year. For the 220 living kidney donor transplants performed in Ontario, Canada, each year, this translates to a total annual cost savings of $2.7M. In conclusion, a more timely evaluation of living donor candidates and their intended recipients may increase the supply of kidneys for transplantation. Improved evaluation efficiency may also yield more pre-emptive transplants and substantial healthcare cost savings through averted dialysis costs.
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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 | 0.009 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".