Characterizing the clinical subgroups of individuals who present to the emergency department for alcohol-related harms in Ontario, Canada: A latent class analysis
Bibliographic record
Abstract
Alcohol-related emergency department (ED) visits are common and associated with adverse clinical outcomes, including premature mortality. This population-based retrospective cohort study identified clinically distinct subgroups of individuals who experience alcohol-related ED visits and characterized differences in the risk of adverse outcomes between them. 73,658 individuals who experienced an alcohol-related ED visit in Ontario, Canada between 2017 and 2018 were identified. Latent class analysis (LCA) revealed five clinically distinct subgroups within the overall cohort. These subgroups followed a severity gradient from low-frequency service use for acute intoxication to high-frequency service use for alcohol use disorder (AUD) and related comorbidities. Relative to those presenting for acute intoxication, those presenting for AUD and comorbidities had a much higher risk of hospital admission (adjusted odds ratio [aOR]: 8.26, 95 % confidence interval [CI]: 7.81-8.75) and post-discharge mortality (adjusted hazard ratio [aHR]: 3.07, 95 % CI: 2.81-3.37). There was a subgroup of individuals with a history of high frequency alcohol-related health service use who were at the highest risk of experiencing another alcohol-related ED visit after the index event (aHR: 4.76, 95 % CI: 4.55-4.99). Individuals who experience alcohol-related ED visits are not a homogenous population, but a constellation of subgroups with different clinical characteristics and risk of adverse outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".