Supportive care in transplantation: A patient-centered care model to better support kidney transplant candidates and recipients
Bibliographic record
Abstract
Kidney transplantation (KT), although the best treatment option for eligible patients, entails maintaining and adhering to a life-long treatment regimen of medications, lifestyle changes, self-care, and appointments. Many patients experience uncertain outcome trajectories increasing their vulnerability and symptom burden and generating complex care needs. Even when transplants are successful, for some patients the adjustment to life post-transplant can be challenging and psychological difficulties, economic challenges and social isolation have been reported. About 50% of patients lose their transplant within 10 years and must return to dialysis or pursue another transplant or conservative care. This paper documents the complicated journey patients undertake before and after KT and outlines some initiatives aimed at improving patient-centered care in transplantation. A more cohesive approach to care that borrows its philosophical approach from the established field of supportive oncology may improve patient experiences and outcomes. We propose the "supportive care in transplantation" care model to operationalize a patient-centered approach in transplantation. This model can build on other ongoing initiatives of other scholars and researchers and can help advance patient-centered care through the entire care continuum of kidney transplant recipients and candidates. Multi-dimensionality, multi-disciplinarity and evidence-based approaches are proposed as other key tenets of this care model. We conclude by proposing the potential advantages of this approach to patients and healthcare systems.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".