Emergence delirium in children: a Brazilian survey
Bibliographic record
Abstract
BACKGROUND: Pediatric emergence delirium is characterized by a disturbance of a child's awareness during the early postoperative period that manifests as disorientation, altered attention and perception. The incidence of emergence delirium varies between 18% and 80% depending on risk factors and how it is measured. Reports from Canada, Germany, Italy, United Kingdom, and France demonstrated a wide range of preventive measures and definitions, indicating that there is a lack of clarity regarding emergence delirium. We aimed to assess the practices and beliefs among Brazilian anesthesiologists regarding emergence delirium. METHODS: A web-based survey was developed using REDCap®. A link and QR Code were sent by email to all Brazilian anesthesiologists associated with the Brazilian Society of Anesthesiology (SBA). RESULTS: We collected 671 completed questionnaires. The majority of respondents (97%) considered emergence delirium a relevant adverse event. Thirty-two percent of respondents reported routinely administrating medication to prevent emergence delirium, with clonidine (16%) and propofol (15%) being the most commonly prescribed medications. More than 70% of respondents reported a high level of patient and parent anxiety, a previous history of emergence delirium, and untreated pain as risk factors for emergence delirium. Regarding treatment, thirty-five percent of respondents reported using propofol, followed by midazolam (26%). CONCLUSION: Although most respondents considered emergence delirium a relevant adverse event, only one-third of them routinely applied preventive measures. Clonidine and propofol were the first choices for pharmacological prevention. For treatment, propofol and midazolam were the most commonly prescribed medications.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".