Pretransplant Patient Education in Solid-organ Transplant: A Narrative Review
Bibliographic record
Abstract
Education for pretransplant, solid-organ recipient candidates aims to improve knowledge and understanding about the transplant process, outcomes, and potential complications to support informed, shared decision-making to reduce fears and anxieties about transplant, inform expectations, and facilitate adjustment to posttransplant life. In this review, we summarize novel pretransplant initiatives and approaches to educate solid-organ transplant recipient candidates. First, we review approaches that may be common to all solid-organ transplants, then we summarize interventions specific to kidney, liver, lung, and heart transplant. We describe evidence that emphasizes the need for multidisciplinary approaches to transplant education. We also summarize initiatives that consider online (eHealth) and mobile (mHealth) solutions. Finally, we highlight education initiatives that support racialized or otherwise marginalized communities to improve equitable access to solid-organ transplant. A considerable amount of work has been done in solid-organ transplant since the early 2000s with promising results. However, many studies on education for pretransplant recipient candidates involve relatively small samples and nonrandomized designs and focus on short-term surrogate outcomes. Overall, many of these studies have a high risk of bias. Frequently, interventions assessed are not well characterized or they are combined with administrative and data-driven initiatives into multifaceted interventions, which makes it difficult to assess the impact of the education component on outcomes. In the future, well-designed studies rigorously assessing well-defined surrogate and clinical outcomes will be needed to evaluate the impact of many promising initiatives.
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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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".