Effectiveness of Music to Improve Anxiety in Hemodialysis Patients
Bibliographic record
Abstract
Anxiety affects many hemodialysis patients. The response to treatment varies and often requires patients to take therapeutics for long periods; thus, many patients look for complementary approaches. There have been reports of music alleviating anxiety in hemodialysis patients. However, the efficacy of music needs to be evaluated. The objective of this study was to conduct a systematic review and meta-analysis to compare the effects of participation in standard care combined with music with standard care alone. This was a systematic review and meta-analysis of randomized controlled trials to determine the efficacy of music to lower anxiety in hemodialysis patients. Five studies were included in the review (290 patients). Listening to music resulted, on average, in an anxiety reduction that was -0.52 standard deviation units greater (95% confidence interval, -1.02 to -0.03 lower, P = .003) than in the standard care group. No adverse events were identified. All trials contained a risk of bias due to lack of blinding. The heterogeneity showed an I = 75%. The strength of evidence was very low. No adverse events were identified. Few trials were available for inclusion, with small sample sizes and significant heterogeneity. Within these considerable limitations there was a demonstrated decrease in anxiety for hemodialysis patients receiving standard care augmented with music. The effect size was moderate. Results were inconsistent across studies. We are uncertain about the estimate. The likelihood that effect will be substantially different is very high. Further research has a large potential for reducing uncertainty about the effects of the music interventions.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.009 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".