Identifying behaviors that characterize emergence delirium: An observational study
Bibliographic record
Abstract
BACKGROUND: Diagnostic criteria for emergence agitation are sensitive but not specific; they misclassify patients who are angry or upset as having emergence delirium. AIMS: The aim of this three-phase study was to determine expert agreement on the behaviors that differentiate children with emergence delirium from those without. METHODS: In the first phase of this observational study, pediatric dental patients were video recorded while awakening from anesthesia. In the second phase, salient 10 s segments of the recordings showing patient activity were shown to an expert audience of pediatric dentists, anesthesiologists and Post Anesthetic Care nurses, who scored the recordings as showing or not showing "true emergence delirium." In phase 3, the video segments were assessed by three research assistants using a behavior checklist for features that discriminate between those scored "true emergence delirium" and those scored "NOT true emergence delirium" by the experts. RESULTS: One hundred and fifty-four pediatric dental patients were included. Subsequently, an expert audience consisting of 10 anesthesiologists, 12 anesthesiology residents, 3 pediatric dentists, and 4 experienced Post Anesthesia Care Unit nurses rated each 10-second video segment. This resulted in three groups of patients: a group for whom all experts agreed was "True emergence delirium" (n = 33; CI 21 to 45), a group for whom all agreed was "Not True emergence delirium" (n = 120; CI 107 to 133), and a group where experts disagreed (n = 11; CI 4 to 18). Three research assistants then completed a behavior checklist for each of the 33 "True emergence delirium" video segments and matched "Not True" controls. Twenty-four behaviors were identified as significantly different between videos scored True emergence delirium and those scored Not True emergence delirium. Research assistants reached almost perfect agreement (0.81-1.00) on one behavior, and substantial agreement (0.61-0.80) on seven behaviors that characterized "True emergence delirium." CONCLUSIONS: Eight behaviors that differentiate pediatric dental patients with emergence delirium from those without were found. These discriminators may be used to develop a scale that will lead to better diagnosis and treatment of emergence delirium.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".