A nationwide study of liver disease hospitalizations during the coronavirus pandemic in the United States
Bibliographic record
Abstract
BACKGROUND AND AIM: The impact of the Coronavirus disease-2019 (COVID-19) pandemic on patients with liver disease is not well described at the population level in the United States. We used the largest, nationwide inpatient dataset to describe inpatient liver disease outcomes in the United States during the first year of the pandemic (2020) using 2018 and 2019 as comparator years. METHODS: Using the National Inpatient Sample (2018-2020), we explored year-to-year and 2020 month-to-month trends in hospitalizations, length of stay, and inpatient mortality for liver-related complications including cirrhosis, alcohol-associated liver disease (ALD) and alcoholic hepatitis using regression modeling. We reported relative change (RC) in the study period. RESULTS: Decompensated cirrhosis hospitalizations decreased in 2020 compared with 2019 (RC: -2.7%, P < 0.001) while all-cause mortality increased by 15.5% (P < 0.001). Hospitalizations for ALD increased compared with pre-pandemic years (RC: 9.2%, P < 0.001) with a corresponding increase in mortality in 2020 (RC 25.2%, P = 0.002). We observed an increase in liver transplant surgery mortality during the peak months of the pandemic. Importantly, mortality from COVID-19 was higher among patients with decompensated cirrhosis (adjusted odds ratio [OR] 1.72, 95% confidence interval [CI] [1.53-1.94]), Native Americans (OR 1.76, 95% CI [1.53-2.02]), and patients from lower socioeconomic groups. CONCLUSIONS: Cirrhosis hospitalizations decreased in 2020 compared with pre-pandemic years but were associated with higher all-cause mortality rates particularly in the peak months of the COVID-19 pandemic. In-hospital COVID-19 mortality was higher among Native Americans, patients with decompensated cirrhosis, chronic illnesses, and those from lower socioeconomic groups.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".