Streamed Video Clips to Reduce Anxiety in Children During Inhaled Induction of Anesthesia
Bibliographic record
Abstract
BACKGROUND: Anesthesia induction in children is frequently achieved by inhalation of nitrous oxide and sevoflurane. Pediatric anesthesiologists commonly use distraction techniques such as humor or nonprocedural talk to reduce anxiety and facilitate a smooth transition at this critical phase. There is a large body of successful distraction research that explores the use of video and television distraction methods for minor medical and dental procedures, but little research on the use of this method for ambulatory surgery. In this randomized control trial study we examined whether video distraction is effective in reducing the anxiety of children undergoing inhaled induction before ambulatory surgery. METHODS: Children (control = 47, video = 42) between 2 and 10 years old undergoing ambulatory surgery were randomly assigned to a video distraction or control group. In the video distraction group a video clip of the child's preference was played during induction, and the control group received traditional distraction methods during induction. The modified Yale Preoperative Anxiety Scale was used to assess the children's anxiety before and during the process of receiving inhalation anesthetics. RESULTS: All subjects were similar in their age and anxiety scores before entering the operating rooms. Children in the video distraction group were significantly less anxious at induction and showed a significantly smaller change in anxiety from holding to induction than did children in the control group. CONCLUSIONS: Playing video clips during the inhaled induction of children undergoing ambulatory surgery is an effective method of reducing anxiety. Therefore, pediatric anesthesiologists may consider using video distraction as a useful, valid, alternative strategy for achieving a smooth transition to the anesthetized state.
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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 | 0.002 |
| 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) | 0.003 | 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".