Emergency Department Utilization in the United States and Ontario, Canada
Bibliographic record
Abstract
OBJECTIVES: The current crisis in the emergency care system is characterized by worsening emergency department (ED) overcrowding. Lack of health insurance is widely perceived to be a major contributing factor to ED overcrowding in the United States. This study aimed to compare ED visit rates in the United States and Ontario, Canada, according to demographic and clinical characteristics. METHODS: This was a cross sectional study consisting of a nationally representative sample of 40,253 ED visits included in the 2003 National Hospital Ambulatory Medical Care Survey in the United States, and all ED visits recorded during 2003 by the National Ambulatory Care Reporting System in Ontario, Canada. The main outcome was the number of ED visits per 100 population per year. RESULTS: The annual ED visit rate in the United States was 39.9 visits (95% confidence interval = 37.2 to 42.6) per 100 population, virtually identical to the rate in Ontario, Canada (39.7 visits per 100 population). In both the United States and Ontario, Canada, those aged 75 years and older had the highest ED visit rate and women had a slightly higher ED visit rate than men. The most common discharge diagnosis was injury/poisoning, accounting for 25.6% of all ED visits in the United States and 24.7% in Ontario, Canada. Overall, 13.9% of ED patients in the United States were admitted to hospitals, compared with 10.5% in Ontario, Canada. CONCLUSIONS: ED visit rates and patterns are similar in the United States and Ontario, Canada. Differences in health insurance coverage may not have a substantial impact on the overall utilization of emergency care.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".