Temporal and Demographic Trends in Alcohol-Related-Cirrhosis, Alcohol-Related Hepatitis, and Nonalcohol-Related Cirrhosis Hospitalizations From 2012 to 2023: A Population-Based Study in Alberta, Canada
Bibliographic record
Abstract
INTRODUCTION: Hospitalized patients with cirrhosis or alcohol-related hepatitis (AH) have poor clinical outcomes. We used newly validated case definitions to describe temporal trends in AH, alcohol, and nonalcohol-related cirrhosis (AC, NAC) hospitalization rates between 2012 and 2023 in Alberta, Canada. METHODS: We selected all adult (≥20 years) AC, NAC, and AH hospitalizations from the 2012-2023 Discharge Abstract Database. Temporal trends in annual age/sex-adjusted hospitalization rates per 100,000 population were assessed using annual percent change and average annual percent change (AAPC) stratified by sex, age, residency (urban/rural), and area-level income quartile. RESULTS: We identified 25,503 AC, 17,815 NAC, and 2,163 AH hospitalizations. From 2012 to 2023, overall hospitalization rates remained stable for AC (AAPC 0.10%, confidence interval [CI] -0.28 to 0.54) and NAC (AAPC 0.69%, CI -0.42 to 1.88), but increased for AH (AAPC 7.00%, CI 3.79-9.85). AC hospitalization rates increased for people aged 20-34 years (AAPC 16.03%, CI 11.93-21.78) and among the highest area-level income quartile (AAPC 1.84%, CI 1.23-2.51). AH hospitalization rates increased significantly for those aged 20-34 years (AAPC 22.76%, CI 14.82-37.04). During COVID-19, AC and AH hospitalization rates significantly increased, primarily for those in rural areas, and among women and the lowest area-level income quartile for AH. NAC hospitalization rates increased for those 65 years and older (AAPC 2.09%, CI 0.72-3.63), rural men (AAPC 3.12%, CI 0.12-6.50), and rural women (AAPC 4.11%, CI 3.33-4.91). DISCUSSION: Between 2012 and 2023, we report increasing AH hospitalization rates, and stable AC/NAC hospitalization rates. During COVID-19, AC and AH hospitalization rates increased for those from rural areas and lower income areas for AH.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".