Characterising double frequent users in an emergency department
Bibliographic record
Abstract
Visits by frequent users (FUs) has been suggested as one reason for crowding in emergency departments (EDs). In this article, we identified the characteristics of double frequent users (DFUs), ≥ 8 visits during 12 months in an ED during a period of six years, in one ED in Western Sweden. The primary outcome was to characterise DFUs and find common reasons for repeatedly visiting the ED. We conducted a retrospective cohort analysis on register data covering six years of all visits. The DFUs share of all visitors to the ED was not more than 0.03% (144 individuals), but their share of visits was 2.4% (1,017 visits/year). Chest pain and abdominal pain were the most common complaints. A typical DFU is male, around 50-year-old, unemployed, non-immigrant, suffering from alcohol abuse and/or mental health conditions. The results point to the need for changing strategies in ED services towards DFUs suffering from alcohol abuse and/or mental health conditions. The ED prioritises the severely ill but lacks resources and continuity for handling chronic diseases and follow-up routines.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".