Predicting the outcome of liver transplantation in patients with non‐alcoholic steatohepatitis cirrhosis: The NASH LT risk‐benefit calculator
Bibliographic record
Abstract
BACKGROUND: Non-alcoholic Steatohepatitis (NASH) cirrhosis is the second most common indication for liver transplantation (LT) in the US and often is associated with significant co-morbidities. We validated a model and risk prediction score that reflects the benefit derived from LT for NASH cirrhosis by predicting 5-year survival post-LT. METHODS: We developed a prediction score utilizing 6515 NASH deceased donor LT (DDLT) recipients from 2002 to 2019 from the Scientific Registry of Transplant Recipients (SRTR) database to identify a parsimonious set of independent predictors of survival. Coefficients of relevant recipient factors were converted to weighted points to construct a risk scoring system that was then externally validated. RESULTS: The final risk score includes the following independent recipient predictors and corresponding points: recipient age (5 points for age ≥70 years), functional status (3 points for total assistance), presence of TIPSS (2 points), hepatic encephalopathy (1 point), serum creatinine (5 points if >1.45 mg/dl), need for mechanical ventilation (3 points), and dialysis within 1 week prior to LT (7 points). Diabetes is a stratifying variable for baseline risk. Scores range from 0 to 20 with scores above 13 having an overall survival of <65% at 5 years post-LT. Internal and external validation indicated good predictive ability. CONCLUSION: Our practically useable and validated risk score helps to identify and stratify candidates who will derive the most long-term benefit from LT for NASH cirrhosis.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".