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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".