Attitudes of Liver Transplant Candidates Toward Organs From Increased‐Risk Donors
Bibliographic record
Abstract
Increased-risk donor (IRD) organs make up a significant proportion of the deceased organ donor pool but may be declined by patients on the waiting list for various reasons. We conducted a survey of patients awaiting a liver transplant to determine the factors leading to the acceptance of an IRD organ as well as what strategies could increase the rate of acceptance. Adult liver transplant candidates who were outpatients completed a survey of 51 questions on a 5-point Likert scale with categories related to demographics, knowledge of IRDs, and likelihood of acceptance. A total of 150 transplant candidates completed the survey (age 19-80 years). Male patients constituted 67.3%. Many patients (58.7%) had postsecondary education. Only 23.3% of patients had a potential living donor, and 58/144 (40.3%) were not optimistic about receiving an organ in the next 3 months. The overall IRD organ acceptance rate was 41.1%, whereas 26.2% said they would decline an IRD organ. Women were more likely to accept an IRD organ (54.3% versus 34.7%; P = 0.02). Those who had a college education or higher tended to have lower IRD organ acceptability (28.3% versus 47.4%; P = 0.07). Acceptability also increased as the specified transmission risk of human immunodeficiency virus or hepatitis C virus decreased (P < 0.001). Patients were also more likely to accept an IRD organ if they were educated on the benefits of IRD organs (eg, knowledge that an IRD organ was of better quality increased overall acceptance from 41.1% to 63.3%; P < 0.001). Our survey provides insight into liver transplant candidates who would benefit from greater education on IRD organs. Strategies targeting specific educational points are likely to increase acceptability.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".