Music is Beneficial for Awake Craniotomy Patients: A Qualitative Study
Bibliographic record
Abstract
OBJECTIVES: Patients undergoing awake craniotomy may experience high levels of stress. Minimizing anxiety benefits patients and surgeons. Music has many therapeutic effects in altering human mood and emotion. Tonality of music as conveyed by composition in major or minor keys can have an impact on patients' emotions and thoughts. Assessing the effects of listening to major and minor key musical pieces on patients undergoing awake craniotiomy could help in the design of interventions to alleviate anxiety, stress and tension. METHODS: Twenty-nine patients who were undergoing awake craniotomy were recruited and randomly assigned into two groups: Group 1 subjects listened to major key music and Group 2 listened to minor key compositions. Subjects completed a demographics questionnaire, a pre- and post-operative Beck Anxiety Inventory (BAI) and a semi-structured open-ended interview. RESULTS were analyzed using modified thematic analysis through open and axial coding. RESULTS: Overall, patients enjoyed the music regardless of the key distinctions and stated they benefitted from listening to the music. No adverse reactions to the music were found. Subjects remarked that the music made them feel more at ease and less anxious before, during and after their procedure. Patients preferred either major key or minor key music but not a combination of both. Those who preferred major key pieces said it was on the basis of tonality while the individuals who selected minor key pieces stated that tempo of the music was the primary factor. CONCLUSION: Overall, listening to music selections was beneficial for the patients. Future work should further investigate the effects of audio interventions in awake surgery through narrative means.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".