Characteristics of Recurrent Utilization in Pediatric Emergency Departments
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Nationally, frequent utilizers of emergency departments (EDs) are targeted for quality improvement initiatives. The objective was to compare the characteristics and ED health services of children by their ED visit frequency. METHODS: A retrospective study in 1,896,547 children aged 0 to 18 years with 3,263,330 visits to 37 EDs in 2011. The number of ED visits per child within 365 days of their first visit was counted. Patient characteristics (age, chronic condition) and ED care (medications, testing [laboratory and radiographic], and hospital admission) were assessed. We evaluated the relationship between patient characteristics and ED health services received with multivariable regression. RESULTS: Children with ≥4 ED visits (8%) accounted for 24% of all visits and 31% ($1.4 billion) of all costs. As visit frequency increased from 1 to ≥4, the percentage of children aged <1 year increased (12.1% to 33.2%) and the percentage of children without a chronic condition decreased (81.9% to 45.6%) (P < .001 for both). Children with ≥4 ED visits had a higher percentage of visits without medication administration (aside from acetaminophen or ibuprofen), testing, or hospital admission when compared with children with 1 visit (35.4% vs 29.0%; P < .001). Children with ≥4 ED visits who were aged <1 year (odds ratio: 3.8; 95% confidence interval: 3.7-3.9) and who were without a chronic condition (odds ratio: 3.1; 95% confidence interval: 3.0-3.1) had the highest likelihood of experiencing this type of visit. CONCLUSIONS: With a disproportionate share of pediatric ED cost and utilization, frequent utilizers, especially infants without a chronic condition, are the least likely to need medications, testing, and hospital admission.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".