Patterns and Predictors of Mortality After Waitlist Dropout of Patients With Hepatocellular Carcinoma Awaiting Liver Transplantation
Bibliographic record
Abstract
BACKGROUND: There is a lack of information about survival after dropout from the liver transplant waiting list. Therefore, we aimed to assess the overall survival, and risk factors for death, after waiting list dropout due to hepatocellular carcinoma (HCC) progression. METHODS: We assessed patients who dropped out of the liver transplant waiting list between 2000 and 2016 in a single, large academic North American center. Patients were divided into 3 groups according to the types of HCC progression: locally advanced disease (LAD), extrahepatic disease (EHD), and macrovascular invasion (MVI). The primary outcome was overall survival. Survival was assessed by the Kaplan-Meier method. Predictors of death after dropout were assessed by multivariable Cox regression. RESULTS: During the study period, 172 patients dropped out due to HCC progression. Of those, 37 (21.5%), 74 (43%), and 61 (35.5%) dropped out due to LAD, EHD, and MVI, respectively. Median survival according to cause of dropout (LAD, EHD, or MVI) was 1.0, 4.4, or 3.3 months, respectively (P = 0.01). Model for End-stage Liver Disease (MELD) score (hazard ratio [HR], 1.04; 95% confidence interval [CI], 1.01-1.08), alcoholic liver disease (HR, 1.66; 95% CI, 1.02-2.71), and α-fetoprotein >1000 ng/mL (HR, 1.86; 95% CI, 1.22-2.84) were predictors of mortality after dropout. Dropout due to EHD (HR, 0.61; 95% CI, 0.38-0.98) and undergoing treatment after dropout were protective factors (HR, 0.32; 95% CI, 0.21-0.48) for death. CONCLUSIONS: Patient prognosis after dropout is dismal. However, a subgroup of patients may have longer survival. The present study identifies the patterns of waitlist dropout in patients with HCC and provides evidence for the effectiveness of treatment strategies offered to HCC patients after dropout.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".