The effect of ketamine on the separation anxiety and emergence agitation in children undergoing brief ophthalmic surgery under desflurane general anesthesia
Bibliographic record
Abstract
BACKGROUND: Emergence agitation (EA) frequently occurs after desflurane anesthesia in children. Ketamine, because of its sedative and analgesic properties, might be useful for the management of separation anxiety and EA. We investigated the preventive effect of ketamine on separation anxiety and EA after desflurane anesthesia in children for brief ophthalmic surgery. METHODS: Sixty children, ranging in age from 2-8 years old, undergoing brief ophthalmic surgery were randomly allocated to one of the 3 groups: group C received normal saline, group K1.0 received ketamine 1.0 mg/kg intravenously before entering the operating room, or group K0.5 received ketamine 0.5 mg/kg 10 min before the end of the surgery. Before induction, the separation anxiety score was evaluated. Extubation time, post-anesthesia care unit stay time, postoperative nausea and vomiting, emergence agitation, and pain were assessed. RESULTS: The group K1.0 had a lower separation anxiety score compared with groups K0.5 and C. Extubation time in group K0.5 was significantly prolonged compared with groups K1.0 and C. The incidence of EA and the modified Children's Hospital of Eastern Ontario Pain Scale were significantly lower in group K1.0 and group K0.5 compared to group C, but there was no significant difference between groups K1.0 and K0.5. CONCLUSIONS: In children undergoing brief ophthalmic surgery with desflurane anesthesia, ketamine 1.0 mg/kg administered before entering the operating room reduced separation anxiety, postoperative pain, and incidence of EA without delay in recovery.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".