Level of Acuity in Pediatric Patients with Recurrent Emergency Department Visits
Bibliographic record
Abstract
Introduction: Recurrent ED utilizers account for a substantial proportion of ED visits, yet little data exists on children with multiple visits. The objective of this study was to compare the need for interventions and triage acuity of recurrent utilizers of a pediatric emergency department to that of non-recurrent utilizers. Methods: This is a retrospective analysis of children presenting to a pediatric emergency department. Children were classified as recurrent utilizers if they had 4 or more visits to the ED per year and non-recurrent utilizers if they had less than 4 visits. Data was collected and inter-group comparison performed on critical interventions received (admission, consultation, intravenous fluid therapy, observation, and performance of procedures), all interventions received (including critical interventions as well as laboratories, radiographs, and medications), and triage acuity for the index visit. Results: Two-hundred thirty patients were included, of whom, 15% were classified as recurrent utilizers. This group had significantly lower rates of requiring a critical intervention (8.6% vs. 51.4%, p=.001), lower rates of any intervention (51.4% vs. 74.4%, p=.007), and less urgent triage acuity (3.3 vs. 3.1, p=.029). Conclusions: Recurrent utilizers of the pediatric emergency department had significantly lower need for intervention and less urgent mean triage acuity when compared with non-recurrent utilizers.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".