Factors Associated With Discharge Home After Transfer to a Pediatric Emergency Department
Bibliographic record
Abstract
OBJECTIVES: The transfer of children from community emergency departments (EDs) to tertiary care pediatric EDs for investigations, interventions, or a second opinion is common. In order to improve health care system efficiency, we must have a better understanding of this population and identify areas for education and capacity building. METHODS: We conducted a retrospective chart review of all patients (aged 0-17 years) who were transferred from community ED to a pediatric ED from November 2013 to November 2014. The primary outcome was the frequency of referred patients who were discharged home from the pediatric ED. RESULTS: Two hundred four patients were transferred from community EDs in the study period. One hundred thirteen children (55.4%) were discharged home from the pediatric ED. Presence of inpatient pediatric services (P = 0.04) at the referral hospital and a respiratory diagnosis (P = 0.03) were independently associated with admission to the children's hospital. In addition, 74 patients (36.5%) had no critically abnormal vital signs at the referral hospital and did not require any special tests, interventions, consultations, or admission to the children's hospital. Younger age (P = 0.03), lack of inpatient pediatric services (P = 0.04), and a diagnosis change (P = 0.03) were independently associated with this outcome. CONCLUSIONS: More than half of patients transferred to the pediatric tertiary care ED did not require admission, and more than one third did not require special tests, interventions, consults, or admission. Many of these patients were likely transferred for a second opinion from a pediatric emergency medicine specialist. Education and real-time videoconferencing consultations using telemedicine may help to reduce the frequency of transfers for a second opinion and contribute to cost savings over the long term.
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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 | 0.000 | 0.008 |
| 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 | 0.001 | 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".