Approaching End-of-Life Care in Organ Transplantation: The Impact of Transplant Patients' Death and Dying
Bibliographic record
Abstract
Despite the success of transplantation, many transplant candidates and transplant recipients die each year. Some die awaiting transplants and some die months or years after receiving an organ. Quality end-of-life care can play a valuable role in easing the impact of death and dying in transplantation, as it focuses on enhancing patients' quality of life near death. Quality end-of-life care recognizes the values and preferences of patients and their families, and involves a process of shared decision making about patients' healthcare treatment in collaboration with healthcare practitioners. Advance care planning involves discussions with patients about their wishes and values about care, in the event that the patient becomes incapable of making such decisions. This article focuses on the application to transplantation of quality end-of-life care and advance care planning and identifies the effects that death and dying of transplant patients have on others. The information herein encourages healthcare practitioners to view and deliver quality end-of-life care as part of transplant patients' overall treatment management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".