Emergency department use
Bibliographic record
Abstract
Objective To determine if having a primary care provider is an important factor in frequency of emergency department (ED) use. Design Analysis of a central computerized health network database. Setting Three EDs in southern New Brunswick. Participants All ED visits during 1 calendar year to an urban regional hospital (URH), an urban urgent care centre (UCC), and a rural community hospital (RCH) were captured. Main outcome measures Patients with and without listed primary care providers were compared in terms of number of visits to the ED. A logistic regression analysis was used to determine factors predictive of frequent attendance. Results In total, 48 505, 41 004, and 27 900 visits were made to the URH, UCC, and RCH, respectively, in 2009. The proportion of patients with listed primary care providers was 36.6% for the URH, 37.1% for the UCC, and 89.4% for the RCH. Among ED patients at all sites, frequent attenders (4 or more visits to an ED in 1 year) were significantly more likely (59.6% vs 45.1%, P < .001) to have listed primary care providers. Other factors that predicted frequent use included attendance at a rural ED, female sex, and older age. Conclusion This study characterizes attendance rates for 3 EDs in southern New Brunswick. Our findings highlight interesting differences between urban and rural ED populations, and suggest that frequent use of the ED might not be related to lack of a listed primary care provider.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".