Ketamine is effective in decreasing the incidence of emergence agitation in children undergoing dental repair under sevoflurane general anesthesia
Bibliographic record
Abstract
BACKGROUND: Emergence agitation or delirium is a known phenomenon that may occur in children undergoing general anesthesia with inhaled agents. Our aim was to test the hypothesis that the addition of a small dose of ketamine at the end of sevoflurane anesthesia will result in a decrease in the incidence and severity of such phenomenon. METHODS: We performed a randomized double blind study involving 85 premedicated children 4-7 years old undergoing dental repair. Children were premedicated with acetaminophen and midazolam. Anesthesia was induced and maintained with sevoflurane in N2O/O2. Group K received ketamine 0.25 mg.kg (-1) and Group S received saline. We evaluated recovery characteristics upon awakening and during the first 30 min using the Pediatric Anesthesia Emergence Delirium scale. RESULTS: Eighty of the 85 enrolled children completed the study. There were 42 children in Group I. Emergence agitation was diagnosed in seven children in the ketamine group (16.6%) and in 13 children in the placebo group (34.2%). There was no difference in time to meet recovery room discharge criteria between the two groups. CONCLUSIONS: We conclude that the addition of ketamine 0.25 mg.kg(-1) can decrease the incidence of emergence agitation in children after sevoflurane general anesthesia.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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 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".