Factors and outcomes associated with paediatric emergency department arrival patterns through the day
Bibliographic record
Abstract
INTRODUCTION: Steadily increasing emergency department (ED) utilization has prompted efforts to increase resource allocation to meet demand. Little is known about the distribution and characteristics of patient arrivals by time of day. This study describes the variability and patterns of ED resource utilization related to patient, acuity, clinical, and disposition characteristics over a 24-hour period. METHODS: Retrospective cross-sectional study of all visits to a tertiary children's hospital over a 1-year period. We use descriptive statistics to present ED visit details stratified by shift of arrival, and multivariable regression to explore the association between shift of presentation and hospital admission at index and 7-day return ED visits. RESULTS: Of 46,942 visits during the study period, 12% arrived overnight, 42% during the day, and 45% during the evening with variability in pattern of shift arrival by day of week. Overnight arrivals had a higher acuity (Canadian Triage and Acuity Scale [CTAS]) and different presenting complaints (more viral infection, less minor trauma) than day and evening arrivals, but similar ED length of stay. Shift of arrival was not associated with admission to hospital, but age, gender, socioeconomic status (SES), and day of week were. DISCUSSION: ED utilization patterns vary by shift of arrival. Though overnight arrivals represent a smaller proportion of total daily arrivals, their acuity is higher, and the spectrum of disease differs from day or evening arrivals. CONCLUSIONS: Understanding variations and patterns of ED utilization by shift of arrival and day of week may be helpful in tailoring resource allocation to more accurately and specifically meet demands.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".