Identifying subgroups and risk among frequent emergency department users in British Columbia
Bibliographic record
Abstract
Objective: Frequent emergency department (ED) users are heterogeneous. We aimed to identify subgroups and assess their mortality. Methods: We identified patients ≥18 years with ≥1 ED visit in British Columbia from April 1, 2012 to March 31, 2015, and linked to hospitalization, physician billing, prescription, and mortality data. Frequent users were the top 10% of patients by ED visits. We employed cluster analysis to identify frequent user subgroups. We assessed 365-day mortality using Kaplan-Meier curves and conducted Cox regressions to assess mortality risk factors within subgroups. Results: We identified 4 subgroups. Subgroup 1 (“Elderly”) had median age 77 years (interquartile range [IQR]: 66–85), 5 visits/year (IQR: 4–6), median 8 prescription medications (IQR: 5–11), and 24.7% mortality. Subgroup 2 (“Mental Health and Alcohol Use”) had median age 48 years (IQR: 34–61), 13 visits/year (IQR: 10–16), and 12.3% mortality. They made a median 31 general practitioner visits (IQR: 19–51); however, only 23.7% received a majority of services from 1 primary care physician. Subgroup 3 (“Young Mental Health”) had median age 39 years (IQR: 28–51), 5 visits/year (IQR: 4–6), and 2.2% mortality. Subgroup 4 (“Short-term”) had median age 50 years (IQR: 34–65), 4 visits/year (IQR: 4–5) regularly spaced over a short term, and 1.4% mortality. Male sex (all subgroups), long-term care (“Mental Health and Alcohol Use;” “Young Mental Health”), and rural residence (“Elderly” in long-term care; “Young Mental Health”) were associated with increased mortality. Conclusions: Our results identify frequent user subgroups with varying mortality. Future research should explore subgroups’ unmet needs and tailor interventions toward them.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".