Music Decreases Sedative Requirements During Spinal Anesthesia
Bibliographic record
Abstract
UNLABELLED: Ambulatory surgery can create significant anxiety. This prospective study measured whether music can influence anxiety and perioperative sedative requirements in outpatients undergoing surgery with spinal anesthesia. We also evaluated the correlation between two anxiety measures, the State-Trait Anxiety Inventory test (STAI) and the 0- to 10-cm visual analog scale (VAS 0-10), with 0 meaning complete relaxation and 10 the worst feeling of anxiety possible. Fifty unpremedicated patients were randomly assigned to listen to music of their choice via headset during the perioperative period (Group I) or to have no music (Group II). All participants used patient-controlled IV midazolam sedation and underwent repeated evaluations of their anxiety level with the STAI and the VAS 0-10. Midazolam requirements during surgery (Group I, 0.6 +/- 0.7 versus Group II, 1.3 +/- 1.1 mg; P < 0.05) and for the whole perioperative period (Group I, 1.2 +/- 1.3 versus Group II, 2.5 +/- 2.0 mg; P < 0.05) were smaller in patients listening to music. Anxiety levels, measured with STAI or VAS 0-10, were similar in both groups. The Spearman's coefficient values between STAI and VAS 0-10 ranged from 0.532 to 0.687. We conclude that patients listening to music require less midazolam to achieve a similar degree of relaxation as controls and that measures of anxiety obtained from the STAI and the VAS 0-10 are positively, but only moderately, correlated. IMPLICATIONS: It is possible to decrease sedative requirements during surgery under spinal anesthesia by allowing patients to listen to music to reduce their anxiety.
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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) | 0.002 | 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".