Seasonal Variations of Hospital Admissions for Alcohol-Related Hepatitis in the United States
Bibliographic record
Abstract
Background: Clinical experience suggests an increased hospitalization rate for alcohol-related hepatitis (AH) in the winter months; however, seasonal variations in the prevalence of hospitalizations for AH have not been described previously. We hypothesized that AH hospitalizations would be higher in the winter months due to the holiday season and increased alcohol sales. Methods: Patients with primary or secondary discharge diagnosis of AH were included in the study (International Classification of Diseases, Clinical Modification-10th Revision codes K70.4 and K70.1) between January 2016 and December 2019. The primary outcome measure for this study was daily hospitalizations by each month of the year. Secondary outcome measures included the rate of in-hospital mortality associated with AH, for each month. Results: The highest number of AH-related admissions was reported in July (n = 56,800; 9%), followed by August (n = 55,700; 8.8%) and May (n = 54,865; 8.7%). February had the lowest number of admissions (n = 46,550; 7.37%). The adjusted mortality was highest in December (overall mortality: 9.6%; adjusted odds ratio: 1.29; 95% confidence interval: 1.142 - 1.461; P < 0.0001) and lowest in May (overall mortality rate: 7.7%). No difference was noted between length of stay and total hospitalization cost between months. Conclusion: Our findings demonstrate that seasonal variations in hospitalizations related to AH do exist across the United States. Regional differences also exist and follow unique patterns. The increase in admissions for AH is in line with other studies suggesting that heavy drinking happens during the warm season. Hospital administrators and other stewards of healthcare resources can use seasonal patterns to guide allocation of resources.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".