Adult liver transplantation: what non-specialists need to know
Bibliographic record
Abstract
#### Summary points Potential transplant recipients often outnumber donors Improved donor schemes, broader donor criteria, split liver grafts, and live donors (who donate a portion of their liver) can increase the number of transplants Long term survival after transplant is excellent Family doctors are important in the management and monitoring of hypertension, diabetes, hyperlipidaemia, and renal function, and in cancer surveillance after the transplant The prevention of end stage liver disease and the early detection of liver complications could reduce the number of transplants needed #### Sources and selection criteria There have been few large-scale randomised controlled trials and Cochrane reviews on liver transplantation; data mostly come from extensive registry descriptions, multiple case-series, or small trials. We have combined our knowledge with that published in recent guidelines and in articles identified by Pubmed searches with the term liver transplantation. The UK has approximately 6 000 surviving liver transplant recipients,1 and annually about 600 people with liver disease receive new livers. The post-transplant population is growing, since nearly 550 of those 600 patients are alive one year after the operation, and at least 300 are alive 20 years after.2 3 We review liver transplantation for a non-specialist audience, with an emphasis on transplants in adults. Common triggers for referral in people with chronic, usually cirrhotic, liver disease are progressive jaundice, diuretic-resistant ascites, or hepatocellular carcinoma (box 1). Early referral is recommended since patients vary in their disease progression, and the assessment must be thorough. Liver transplantation is principally aimed at restoring health and improving survival, but, unlike other operations with similar potential benefit, limited resources restrict the number of people who receive transplants. #### Box 1 Underlying indications that can lead to transplantation ##### Liver failure ###### Acute/subacute (approximately 10% of transplants)
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.049 | 0.013 |
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".