Religion and Attitudes Toward Xenotransplantation: Results of a Nationwide Survey in the United States
Bibliographic record
Abstract
Religious viewpoints have been shown to influence the ways in which many persons approach medical decision-making and have been noted as a potential barrier to xenotransplantation acceptance. This study sought to explore how attitudes toward xenotransplantation differ among various religious beliefs. A national Likert-scale survey was conducted in 2023 with a representative sample in the United States. Religious belief was self-reported. Regression analysis was used to identify associations with religious belief and hesitations about xenotransplantation. Five thousand and eight individuals across the United States responded to the survey. The two biggest concerns about xenotransplantation across religious groups were the current lack of evidence about success and the risk of xenozoonosis. Although they still expressed concerns about certain issues, Catholic and Muslim respondents were most comfortable with xenotransplantation for all. On average, the risk of xenozoonosis was a concern among 25% across all religious beliefs (p <0.0001). Orthodox Christians expressed the highest rate of negative feelings toward the recent xenotransplantation experiments on brain dead and living individuals. Those who reported no religion were most likely to have negative feelings about killing pigs for human organ transplant (OR 1.26; 95% CI: 1.08-1.46). As xenotransplantation progresses from pre-clinical studies to clinical trials, and potentially to clinical therapy, hesitations among religious groups exist. Specific studies should be designed to investigate how religious viewpoints can affect xenotransplantation acceptance.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".