Divergent trajectories of lean <i>vs </i>obese non-alcoholic steatohepatitis patients from listing to post-transplant: A retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: Non-alcoholic steatohepatitis (NASH) cirrhosis is the second most common indication for liver transplantation (LT). The role of body mass index (BMI) on outcomes of NASH cirrhosis has been conflicting. AIM: obese NASH cirrhosis, from listing up to post-transplant, having adjusted their BMI for ascites. METHODS: We retrospectively reviewed all adult NASH patients listed for LT in our program from 2012 to 2019. Fine-Gray Competing Risk analyses and Cox Proportional-Hazard Models were performed to examine the cumulative incidence of transplant and survival outcomes respectively. RESULTS: = 0.0142) with 83% lower instantaneous rate of death in obese group. CONCLUSION: Although lean NASH is considered to be more benign than obese NASH, our study suggests a paradoxical correlation of lean NASH with waitlist outcomes, and graft and patient survival post-LT.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".