Partnering with Patients to Enhance Access to Kidney Transplantation and Living Kidney Donation
Bibliographic record
Abstract
Kidney transplantation gives many patients with kidney failure a longer and healthier life.Unfortunately, some transplant-eligible patients will never receive one.In this paper, we describe how patients and researchers collaborated on new strategies and programs to enhance access to kidney transplantation and living kidney donation.These efforts led to the creation of the Transplant Ambassador Program (TAP).TAP is a patient-led program that helps connect patients who have kidney failure to individuals who have successfully received a kidney transplant or donated a kidney.We also detail barriers, facilitators and lessons learned from engaging patients in research.* Lead co-authors.P = Patient partner. Key Points• Patients and researchers collaborated to enhance access to kidney transplant.Efforts led to the creation of the Transplant Ambassador Program -a patient-led program that connects patients who have kidney failure with kidney transplant recipients.• Several facilitators, barriers and lessons learned from engaging patients in research were identified.Some facilitators included biweekly meetings with patients and the research team to sustain engagement and keep an open line of communication.• Patient partners were involved throughout the study, from study development to manuscript preparation, which enriched the research and ensured that the research is meaningful to patients and healthcare professionals.
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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 | 0.015 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".