Geographic disparities in access to liver transplant for advanced cirrhosis: Time to ring the alarm!
Bibliographic record
Abstract
Decompensated cirrhosis and hepatocellular cancer are major risk factors for mortality worldwide. Liver transplantation (LT), both live-donor LT or deceased-donor LT, are lifesaving, but there are several barriers toward equitable access. These barriers are exacerbated in the setting of critical illness or acute-on-chronic liver failure. Rates of LT vary widely worldwide but are lowest in lower-income countries owing to lack of resources, infrastructure, late disease presentation, and limited donor awareness. A recent experience by the Chronic Liver Disease Evolution and Registry for Events and Decompensation consortium defined these barriers toward LT as critical in determining overall survival in hospitalized cirrhosis patients. A major focus should be on appropriate, affordable, and early cirrhosis and hepatocellular cancer care to prevent the need for LT. Live-donor LT is predominant across Asian countries, whereas deceased-donor LT is more common in Western countries; both approaches have unique challenges that add to the access disparities. There are many challenges toward equitable access but uniform definitions of acute-on-chronic liver failure, improving transplant expertise, enhancing availability of resources and encouraging knowledge between centers, and preventing disease progression are critical to reduce LT disparities.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".