Overcoming barriers and expanding opportunities in liver transplantation in Mexico
Bibliographic record
Abstract
Liver transplantation (LT) is the only curative treatment for end-stage liver disease. Although Mexico has made important strides in surgical capacity and institutional development, the country continues to report one of the lowest LT rates in Latin America. Multiple challenges remain, including inequitable access to care, limited organ donation, and structural inefficiencies in allocation systems. To review the current status of LT in Mexico, describe historical trends, highlight significant barriers to progress, and discuss potential opportunities for program expansion. We conducted a narrative review incorporating data from the National Transplant Center (Centro Nacional de Trasplantes in Spanish), relevant peer-reviewed literature, and global benchmarks. The analysis focused on trends in liver transplant volume, donor types, etiology shifts, institutional disparities, and the impact of the coronavirus disease 2019 (COVID-19) pandemic. LT activity in Mexico increased from 25 transplants in 1999 to 297 in 2023. However, over 68% of transplants are concentrated in Mexico City, and only eight centers perform more than ten LTs per year. Deceased donors account for most grafts, while living donor transplants remain rare and mostly limited to private institutions. The national waiting list functions primarily as a registry rather than a priority-based allocation system. The COVID-19 pandemic further disrupted transplant programs, particularly in the public sector. Innovative approaches such as donation after circulatory death, hepatitis C virus-positive donor utilization, and advanced perfusion technologies are currently unavailable or underutilized in Mexico. Mexico's LT system faces geographic, regulatory, and resource-related limitations. To improve outcomes and ensure equitable access, strategic reforms focused on donor expansion, centralized allocation, perfusion technologies, and standardization of care are urgently needed.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".