How safe are paediatric emergency departments? A national prospective cohort study
Bibliographic record
Abstract
BACKGROUND: Despite the high number of children treated in emergency departments, patient safety risks in this setting are not well quantified. Our objective was to estimate the risk and type of adverse events, as well as their preventability and severity, for children treated in a paediatric emergency department. METHODS: Our prospective, multicentre cohort study enrolled children presenting for care during one of 168 8-hour study shifts across nine paediatric emergency departments. Our primary outcome was an adverse event within 21 days of enrolment which was related to care provided at the enrolment visit. We identified 'flagged outcomes' (such as hospital visits, worsening symptoms) through structured telephone interviews with patients and families over the 21 days following enrolment. We screened admitted patients' health records with a validated trigger tool. For patients with flags or triggers, three reviewers independently determined whether an adverse event occurred. RESULTS: We enrolled 6376 children; 6015 (94%) had follow-up data. Enrolled children had a median age of 4.3 years (IQR 1.6-9.8 years). One hundred and seventy-nine children (3.0%, 95% CI 2.6% to 3.5%) had at least one adverse event. There were 187 adverse events in total; 143 (76.5%, 95% CI 68.9% to 82.7%) were deemed preventable. Management (n=98, 52.4%) and diagnostic issues (n=36, 19.3%) were the most common types of adverse events. Seventy-nine (42.2%) events resulted in a return emergency department visit; 24 (12.8%) resulted in hospital admission; and 3 (1.6%) resulted in transfer to a critical care unit. CONCLUSION: In this large-scale study, 1 in 33 children treated in a paediatric emergency department experienced an adverse event related to the care they received there. The majority of events were preventable; most were related to management and diagnostic issues. Specific patient populations were at higher risk of adverse events. We identify opportunities for improvement in care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".