Impact of music on anxiety and pain control during extracorporeal shockwave lithotripsy
Bibliographic record
Abstract
BACKGROUND: The present evidence is insufficient for evaluating the impact of exclusive music therapy on anxiety and pain control in extracorporeal shock wave lithotripsy (ESWL). METHODS: A systematic review and meta-analysis was conducted to explore the efficacy of music therapy in reducing pain and anxiety in patients undergoing ESWL. PubMed, Web of Science, Embase, EBSCO, and Cochrane library databases (updated March 2020) were searched for randomized controlled trials assessing music therapy in reducing pain and anxiety in patients undergoing ESWL. The search strategy and study selection process were managed according to the Preferred Reporting Items for Systematic Reviews and Meta-analysis statement. RESULTS: Five randomized controlled trials were included in the meta-analysis. Overall, music intervention groups experienced significant reductions in pain (risk ratios = -1.20, 95% confidence intervals = -1.95 to -0.45, P = .002) and anxiety (risk ratios = -3.31, 95% confidence intervals = -4.97 to -1.84, P < .0001) compared with control groups during ESWL. Music therapy gave patient more satisfaction with the treatment and a willingness to repeat the therapy was reported. However, there was no significant difference in the stone clearance rate. CONCLUSIONS: Listening to music can reduce patient's pain and anxiety significantly with increased therapy satisfaction and willingness to repeat.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.009 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".