Defining the Living Donor Transplant Evaluation Process for Optimization of a One-Day Evaluation Program
Bibliographic record
Abstract
Background: Living donor transplantation provides patients with end stage kidney disease increased longevity and quality of life compared with dialysis. The donor evaluation process can be inefficient and costly for patients and the healthcare system. There is a paucity of research on evaluation optimization in living kidney transplantation. We investigated our living donor evaluation process to develop a one-day program, improving program efficiency. Methods: Living donor staff and patient partner from The Ottawa Hospital Living Kidney Donor program participated in individual, semi-structured interviews to develop a Lucidchart process map of the donor evaluation process and ascertain the time associated with each step. A one-day evaluation program model was developed based on our process map and interview participant feedback. Amount of time for each step of the process was collected for future cost assessment. Results: Mean time to complete the evaluation process and reach donor approval is 9 months. The donor evaluation process can be divided into 3 phases: Initial Interview, Phase I, and Phase II. Phase I requires the most nursing and administrative time. The greatest barriers to process efficiency are 24-hour urine collections to estimate kidney function and coordinator time spent on correspondence with laboratories. A one-day evaluation will reduce the evaluation process and approval to approximately 4 weeks. Greatest barriers for patients included need for increased education and time off work. Next steps will include cost estimates of the current program with the goal of implementing a one-day evaluation program at The Ottawa Hospital. Conclusions: A one-day evaluation program will increase the efficiency of the living donor process for donors, coordinators, and recipients. Phase I investigations are a barrier to program efficiency and can be streamlined with a one-day evaluation. The development of donor educational resources will improve the donation experience for patients.
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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 | 0.072 | 0.085 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".