Prevention of Emergence Agitation After Sevoflurane Anesthesia for Pediatric Cerebral Magnetic Resonance Imaging by Small Doses of Ketamine or Nalbuphine Administered Just Before Discontinuing Anesthesia
Bibliographic record
Abstract
Magnetic resonance imaging (MRI) requires long-lasting immobilization that frequently can only be provided by general anesthesia in pediatric patients. Sevoflurane provides adequate anesthesia but many patients experience emergence agitation. Small doses of ketamine and nalbuphine provide moderate sedation but their benefits have subsided at the time of emergence. We hypothesized that delaying their administration until the end of the procedure would prevent emergence agitation without prolonging patient wake-up and discharge times from the postanesthesia care unit. We performed a double-blind study involving 90 patients (aged 6 mo to 8 yr) randomly allocated to 1 of 3 groups receiving either saline (S-group), ketamine (0.25 mg/kg) (K-group), or nalbuphine (0.1 mg/kg) (N-group) at the end of an MRI procedure under sevoflurane anesthesia. We evaluated emergence conditions, sedation/agitation status and completion of discharge criteria at 30 min. The three groups were comparable in age, sex ratio, physical status, and associated medical disorders. Emergence conditions did not differ significantly. There were significantly more agitated children, at all times, in the S-group and more obtunded patients at early times (5 and 10 min) in both K- and N-groups. All patients met discharge criteria at 30 min but significantly more children were awake and quiet in the K-group and still more in the N-group. In conclusion, small doses of ketamine or nalbuphine administered at the end of an MRI procedure under sevoflurane anesthesia reduce emergence agitation without delaying discharge. Nalbuphine provided better results than ketamine.
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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 | 0.002 |
| 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) | 0.000 | 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".