Cause-specific mortality among patients with cirrhosis in a population-based cohort study in Ontario (2000–2017)
Bibliographic record
Abstract
BACKGROUND: Although patients with cirrhosis are at increased risk of death, the exact causes of death have not been reported in the contemporary era. This study aimed to describe cause-specific mortality in patients with cirrhosis in the general population. METHODS: Retrospective cohort study using administrative health care data from Ontario, Canada. Adult patients with cirrhosis from 2000-2017 were identified. Cirrhosis etiologies were defined as HCV, HBV, alcohol-associated liver disease (ALD), NAFLD, or autoimmune liver disease/other with validated algorithms. Patients were followed until death, liver transplant, or end of study. Primary outcome was the cause of death as liver-related, cardiovascular disease, non-hepatic malignancy, and external causes (accident/self-harm/suicide/homicide). Nonparametric analyses were used to describe the cumulative incidence of cause-specific death by cirrhosis etiology, sex, and compensation status. RESULTS: Overall, 202,022 patients with cirrhosis were identified (60% male, median age 56 y (IQR 46-67), 52% NAFLD, 26% alcohol-associated liver disease, 11% HCV). After a median follow-up of 5 years (IQR 2-12), 81,428 patients died, and 3024 (2%) received liver transplant . Patients with compensated cirrhosis mostly died from non-hepatic malignancies and cardiovascular disease (30% and 27%, respectively, in NAFLD). The 10-year cumulative incidence of liver-related deaths was the highest among those with viral hepatitis (11%-18%) and alcohol-associated liver disease (25%), those with decompensation (37%) and/or HCC (50%-53%). Liver transplant occurred at low rates (< 5%), and in men more than women. CONCLUSIONS: Cardiovascular disease and cancer-related mortality exceed liver-related mortality in patients with compensated 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".