Outcomes in clinical subgroups of patients with alcohol-related hospitalizations: a population-based retrospective cohort study
Bibliographic record
Abstract
ObjectiveIndividuals who experience alcohol-related hospitalizations are at a high risk of recurrent harm and premature mortality. This project characterized the clinical subgroups of individuals who experience alcohol-related hospitalizations to understand who is at the highest risk of recurrent harm following discharge. ApproachPopulation-based retrospective cohort study of individuals with an alcohol-related hospitalization between 2017-2018 in two Canadian provinces (Ontario and Manitoba) using linked provincial health administrative databases. Clinical subgroups were identified with latent class analysis based on the type and frequency of alcohol-related health service use in the two-years preceding the index hospitalization. Associations between subgroup membership, readmission, and mortality in the year following discharge were evaluated using multivariable time-to-event regression. ResultsIn cohorts of 4,753 (Manitoba) and 29,290 (Ontario) individuals, seven subgroups were identified. These followed a severity gradient from low-frequency service use for acute intoxication to high-frequency service use for alcoholic liver disease. Individuals in the ‘liver disease’ subgroup had the highest risk of 1-year mortality relative to the rest of the cohort (adjusted hazard ratio [aHR]: 3.83, 95% confidence interval (CI): 2.80-5.24). A small subgroup of individuals with a history of high-frequency alcohol-related health service had the highest hazard of readmission (aHR: 5.09, 95% CI: 4.11-6.31). Conclusions and ImplicationsThere are distinct clinical subgroups of individuals who experience alcohol related hospitalizations and individuals with high-frequency health service use and alcohol-related liver disease are at the highest risk of readmission and mortality. These subgroups merit consideration in strategies aimed at reducing the risk of post-discharge harm.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".