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Record W3128516671 · doi:10.1097/md.0000000000023684

Impact of music on anxiety and pain control during extracorporeal shockwave lithotripsy

2021· article· en· W3128516671 on OpenAlexaff
Zhenghao Wang, Dechao Feng, Wuran Wei

Bibliographic record

VenueMedicine · 2021
Typearticle
Languageen
FieldPsychology
TopicMusic Therapy and Health
Canadian institutionsImpact
Fundersnot available
KeywordsMedicineExtracorporeal shockwave lithotripsyPain controlAnxietyLithotripsyExtracorporealPhysical therapyGeneral surgerySurgeryPsychiatry

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.493
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

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.

Opus teacher head0.034
GPT teacher head0.347
Teacher spread0.312 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations12
Published2021
Admission routes1
Has abstractyes

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