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 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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".