Patient and health professional preferences for organ allocation and procurement, end-of-life care and organization of care for patients with chronic kidney disease using a discrete choice experiment
Bibliographic record
Abstract
BACKGROUND: Clinical practice, policy and research, and the ethical bases upon which they are founded, should be systematically and transparently informed by both patient and professional values. METHODS: A discrete choice experiment was utilized to understand and quantify the preferences of 351 Canadian patients and healthcare providers in relation to ethically challenging aspects of the management of chronic kidney disease (CKD): procurement and allocation of organs for transplantation, end-of-life care discussions and decision making and the identities of those providing primary care. RESULTS: Patients and health professionals had clear preferences for detailed prognostic information, early advance care planning, shared end-of-life decision making, coordinated models of care that enhance interaction and communication between primary and tertiary care and a more utilitarian approach of best match over first come, first served for allocating deceased donor kidneys. These data also suggest that the innovative strategies of non-directed anonymous donation and paired kidney exchange that are slowly being implemented internationally will be acceptable to both patients and healthcare providers. CONCLUSIONS: Current models of CKD care do not consistently reflect the preferences or priorities of either health professionals or patients.
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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 | 0.032 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".