Emergency department 72-hour revisits among children with chronic diseases: a Saudi Arabian study
Bibliographic record
Abstract
BACKGROUND: Emergency Department (ED) revisits have often been used as an indicator of medical care quality. This study aimed to quantify the frequency of ED revisits within 72 h of discharge and identify its factors among children with chronic diseases. METHODS: We designed a retrospective cohort study of children with at least one chronic disease who were also under 18 years of age and had attended and were discharged from the ED at King Abdullah Specialist Children's Hospital (KASCH-RD), Riyadh, Saudi Arabia between April 19, 2015 and July 29, 2017. The outcome measure was the frequency of ED revisits during a period of 72 h after discharge. RESULTS: The study included 11,057 ED discharges of children with at least one chronic disease. Their revisit rate was 1211 (11%), with 83 (6.9%) having had a second ED revisit within 72 h of ED discharge. According to ICD-10 codes, the most common causes of ED revisits were respiratory, digestive, genitourinary, symptoms, and external causes. Factors of frequent ED revisits within 72 h were young age, institutional health insurance coverage, year of new health information system (2015), external causes, and genitourinary. CONCLUSION: The rate of 72-h ED revisits after discharge of children with chronic diseases treated at KASCH-RD was relatively high, and was associated with young age, institutional health insurance coverage, year of a new health information system implementation, and external causes of ED visit. These study findings amplify the need for intervention to reduce the rate of early ED revisits among children with chronic diseases.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".