Kinder und Jugendliche in deutschen Notaufnahmen
Bibliographic record
Abstract
BACKGROUND: The planned hospital and emergency care reform in Germany aims, among other things, to restructure emergency services towards integrated emergency centers (INZ) and integrated emergency centers for children and adolescents (KINZ). There is a gap in current data on the reasons for presentation and the use of emergency departments by patients under 18 years of age. This study provides a multicenter analysis of the most common reasons for presentation among children and adolescents in German emergency departments. METHODS: In a retrospective, descriptive cross-sectional analysis, data were collected from 251,570 emergency patients under 18 years of age from January 1, 2019, to June 30, 2022, across 22 emergency departments (including three pediatric emergency departments). Reasons for presentation were categorized according to the Canadian Emergency Department Information System-Presenting Complaint List (CEDIS-PCL) and analyzed by age group, gender, and mode of arrival. RESULTS: Over 64.1% of children and adolescents presented with one of the ten most common reasons. In pediatric emergency departments, nontrauma-related reasons, such as respiratory infections and abdominal pain, were predominant, while trauma-related reasons were more frequent in general emergency departments. The gender distribution showed a majority of male patients for trauma-related reasons, whereas some nontrauma-related reasons, like abdominal and headache complaints, were more common among females. Most patients (85.5%) arrived at the emergency department independently; only for seizures did ambulance transport prevail. During the day, 67% of patients presented between 06:00 and 18:00, with 33% presenting in the evening and nighttime hours. CONCLUSIONS: The results show that more than half of children and adolescents present to emergency departments with one of the ten most common chief complaints. Notably, nontraumatological presentations in emergency departments (EDs) highlight that pediatric care also takes place in facilities primarily serving adults. In the future, staff and infrastructure should be appropriately equipped to efficiently ensure the quality of pediatric emergency care on a broad scale. An important approach in this regard is health education and the optimization of access to outpatient care structures.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".