Distraction Therapies for Office‐Based Otolaryngology Procedures Performed on the Upper Airway
Bibliographic record
Abstract
ABSTRACT Objective To assess the effectiveness of auditory and visual distraction interventions on patient discomfort, pain and anxiety during office‐based otolaryngologic upper airway procedures. Data Sources Literature searches were done through Cochrane Central Register of Controlled Trials, Lilacs, MEDLINE, Embase, PsycINFO and Cumulative Index to Nursing and Allied Health Literature. Review Methods The protocol was registered in PROSPERO on August 17th 2022, under Registration number CRD42020204354. Results We identified 138 records; two randomised controlled trials using virtual reality as a distraction technique in adults and one in children were included. All studies had some concerns regarding risk of bias. In adults, anxiety was lower in the virtual reality group than in the standard of care, (mean difference −16.72, 95% CI −27.19 to −6.24, p = 0.002, I 2 = 0%). There was no difference in procedure related pain between groups, (mean difference −0.28, 95% CI −1.24 to 0.68, p = 0.57, I 2 = 10%). There was no difference in satisfaction between groups (standardised mean difference 0.18, 95% CI −0.22 to 0.58, p = 0.37, I 2 = 0%). Only one Paediatric study was included hence no meta‐analysis was done. Anxiety and pain were lower and satisfaction was higher in the group using virtual reality. Conclusions The use of virtual reality distraction in addition to standard analgesia during office‐based otolaryngology upper airway procedures reduced anxiety in adults. It did not decrease pain or increase the level of patient satisfaction. In the paediatric population, there is a reported benefit for procedural anxiety, pain and satisfaction.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".