Children Admitted to the Hospital After Returning to the Emergency Department Within 72 Hours
Bibliographic record
Abstract
OBJECTIVES: Children returning to the emergency department (ED) within 72 hours of their visit may increase overcrowding and health care costs. Identifying the characteristics of returning children who need admission may help distinguish who might need admission on their first visit. The objective of this study was to compare the characteristics of children who returned to the ED and needed admission to the characteristics of those discharged. METHODS: The study used a retrospective chart review of patients 19 years and younger visiting a tertiary pediatric ED during a 1-year period. We excluded patients who left without being seen and those leaving against medical advice. We determined the rate of return visits and then performed χ² and Student t test analyses. Main outcome measures were return and subsequent hospital admission rate to the ED. RESULTS: Of 47,655 eligible children, 2115 (4.4%) returned to the ED within 72 hours. The admission rate for the second visit was 353 (16.7%). There was no significant difference in age, sex, language spoken at home, or time elapsing from the first visit to the re-presentation to the ED between children who needed admission on the returned visit and those discharged when returning. The acuity was significantly lower among children discharged after returning (P < 0.001) but not among those admitted (P < 0.22). CONCLUSIONS: More than 4% of our pediatric ED visits are for children returning within 72 hours. Progression of illness resulting in higher acuity, not age, sex, time from previous visit, or change in chief complaint category, was associated directly with admission on the second visit.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".