Proximity and emergency department use: Multilevel analysis using administrative data from patients with cardiovascular risk factors.
Bibliographic record
Abstract
OBJECTIVE: To determine if geographic proximity to an emergency department (ED) is related to ED use in a metropolitan population of patients with cardiovascular risk factors. DESIGN: Population-based, retrospective cohort study. SETTING: The census metropolitan area of Montreal, Que. PARTICIPANTS: Cohort of 99 400 patients with diabetes, hypertension, or dyslipidemia in 2007 without a history of cardiovascular disease. Each patient was spatially referred to 1 of 5857 dissemination areas (DAs). MAIN OUTCOME MEASURES: Annual number of visits to an ED with respect to the distance between the centroid of a patient's DA and the closest ED, controlling for age, sex, comorbidities, and neighbourhood immigration, social, and material characteristics. Multilevel logistic and negative binomial regressions were used to determine if the proximity to the closest ED was related to ED use, frequent ED use (≥ 4 visits in a year), and number of ED visits. RESULTS: A total of 25 889 (26.0%) patients in the cohort visited an ED at least once during a 1-year period, among which 4563 (4.6%) were frequent users with at least 4 visits. These frequent users were responsible for 28 249 (45.5%) of all 62 021 visits to EDs. The distance between a DA and its closest ED was significantly and negatively correlated with ED use (P < .001), even after controlling for confounding variables. Patients living in a DA close to an ED were also more likely to be frequent users, but the extent of use among them (range from 4 to 82 ED visits) was not related to the distance to the closest ED. CONCLUSION: These results suggest that patients at risk of cardiovascular disease living in a metropolitan area are more likely to seek a medical encounter at the ED if they live closer to it.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".