A scoping review of nonmedical barriers to living donor liver transplant
Bibliographic record
Abstract
This study aims to identify and categorize nonmedical barriers encountered by recipients, donors, and health care providers in the context of living donor liver transplantation (LDLT). Liver transplantation is vital for individuals with liver failure, yet high mortality rates on the transplant waitlist persist. LDLT was introduced to address deceased donor organ shortages; however, its adoption varies widely across regions, prompting the need to explore barriers hindering its implementation. The scoping review employed inclusion and exclusion criteria to identify studies focusing on nonmedical barriers to LDLT in both adult and pediatric populations. Qualitative, quantitative, and mixed-method studies were considered, covering the period from January 2005 to February 2023. The review's search strategy was conducted in the Ovid MEDLINE and Ovid EMBASE databases. Studies meeting the criteria were assessed for their characteristics and findings, which were synthesized into recipient, donor, and provider-level barriers. Among 2394 initially screened articles, 17 studies were eligible for inclusion. Recipient-level barriers encompassed systemic disparities in access, limited social support, immigration status, and inadequate awareness of LDLT. Donor-level barriers involved surgery-related risks, recovery time concerns, financial burdens, and religious beliefs. Provider-level barriers highlighted institutional support inadequacies and specialized surgeon shortages. The scoping review underscores nonmedical barriers to LDLT across recipient, donor, and provider levels. These barriers include socioeconomic disparities, information gaps, and inadequate institutional support. The findings underscore the need for comprehensive national efforts to raise awareness about LDLT and provide essential financial support.
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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 | 0.029 | 0.152 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.030 | 0.035 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".