Best practices to optimize utilization of the National Living Donor Assistance Center for the financial assistance of living organ donors
Bibliographic record
Abstract
Living organ donors face direct costs when donating an organ, including transportation, lodging, meals, and lost wages. For those most in need, the National Living Donor Assistance Center (NLDAC) provides reimbursement to defray travel and subsistence costs associated with living donor evaluation, surgery, and follow-up. While this program currently supports 9% of all US living donors, there is tremendous variability in its utilization across US transplant centers, which may limit patient access to living donor transplantation. Based on feedback from the transplant community, NLDAC convened a Best Practices Workshop on August 2, 2018, in Arlington, VA, to identify strategies to optimize transplant program utilization of this valuable resource. Attendees included team members from transplant centers that are high NLDAC users; the NLDAC program team; and Advisory Group members. After a robust review of NLDAC data and engagement in group discussions, the workgroup identified concrete best practices for administrative and transplant center leadership involvement; for individuals filing NLDAC applications at transplant centers; and to improve patient education about potential financial barriers to living organ donation. Multiple opportunities were identified for intervention to increase transplant programs' NLDAC utilization and reduce financial burdens inhibiting expansion of living donor transplantation in the United States.
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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 | 0.037 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".