Analysis of Plastic Surgery Consultations in a High-Volume Paediatric Emergency Department: A Quality Improvement Initiative
Bibliographic record
Abstract
INTRODUCTION: Consult services influence emergency department (ED) workflow. Prolonged ED length of stay (LOS) correlates with ED overcrowding and as a consequence decreased quality of care and satisfaction of health team professionals. To improve management of paediatric ED patients requiring plastic and reconstructive surgery (PRS) expertise, current processes were analyzed. METHODS: Patient characteristics and metrics of PRS consultations in our paediatric ED were collected over a 3-month period. Data analysis was followed by feedback education intervention to ED and PRS staff. Data collection was then resumed and results were compared to the pre-intervention period. RESULTS: One hundred ninety-eight PRS consultations were reviewed, mean patient age was 6.3 years. Most common (52%) diagnoses were burns and hand trauma; 81% of PRS referrals were deemed appropriate; 25% of PRS consults were requested after hour with no differences in patient characteristics compared to regular hours; 60% of consultations involved interventions in the ED. Time between ED registration and PRS consultation request (116.5 minutes), quality of procedural sedation (52% rated inadequate), and overall ED LOS (289.2 minutes) were identified as main areas of concern and addressed during feedback education intervention. Emergency department LOS and quality of sedation did not improve in the post-intervention period. CONCLUSION: The study provides detailed insights in the characteristics of PRS consultation in the paediatric ED population. Despite high referral appropriateness and education feedback intervention, significant inefficiencies were identified that call for further collaborative efforts to optimize quality of care for paediatric ED patients and improve satisfaction of involved healthcare professionals.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| 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".