Pediatric Emergence Delirium: A Quality Improvement Project at Alberta Children’s Hospital
Bibliographic record
Abstract
Pediatric Emergence Delirium (PED) is a transient state occurring in the early recovery period following general anesthesia, predominantly affecting children aged 2 to 6 years. PED is a common post-anesthetic complication in children, often characterized by agitation, confusion, and disorientation. This quality improvement project was conducted at the Alberta Children’s Hospital to explore potential approaches to reduce PED by administering intraoperative dexmedetomidine and/or post-induction propofol during surgery. The focus was to determine whether dexmedetomidine could reduce the prevalence of PED and whether dexmedetomidine and/or propofol could minimize PED without delaying emergence time. Data collection occurred over two distinct periods: baseline data (n=984) were gathered in spring 2021 following a safety incident that prompted this study, while intervention data (n=514) were collected in fall 2022 after implementing the proposed anesthetic changes. Post-anesthesia care unit (PACU) nurses used the Pediatric Emergence Delirium (PAED) scale to assess PED, with scores over 10 or 12 indicating more severe delirium. The primary outcome of the study suggested that dexmedetomidine reduced the prevalence of PED, as evidenced by lower PAED scores. The secondary outcome showed that both dexmedetomidine and propofol effectively minimized PED without extending emergence time. These findings align with existing literature suggesting that propofol and dexmedetomidine are well-suited for mitigating PED symptoms in pediatric patients undergoing various surgical procedures. However, limitations in the study design, including the more than one-year gap between baseline and intervention data collection, a significant imbalance in patient numbers, the subjective nature of the PAED scale, and potential completion bias in PED assessments, may affect the generalizability of the results. Additionally, the study employed a non-blinded approach, underscoring the need for further research under more controlled conditions. This study contributes to a growing interest in developing tailored anesthetic protocols to enhance postoperative recovery and comfort for pediatric patients, with implications for improving care standards in pediatric anesthesiology. Acknowledgements: Department of Anesthesiology, Perioperative and Pain Medicine, University of Calgary, Cumming School of Medicine
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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 | 0.013 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".