A web app-based music intervention reduces experimental thermal pain: A randomized trial on preferred versus least-liked music style
Bibliographic record
Abstract
Digital technologies are increasingly being used to strengthen national health systems. Music is used as a management technique for pain. The objective of this study is to demonstrate the effects of a web app-based music intervention on pain. The participants were healthy adults and underwent three conditions: Conditioned Pain Modulation (CPM), Most-Liked Music (MLM) and Least-Liked Music (LLM). The music used is MUSIC CARE©, a web app-based personalized musical intervention (“U” Sequence based on a musical composition algorithm). Thermal pain was measured before starting the 20-min music intervention and after three time points for each music condition: 2.20, 11.30, and 20 min. Mean pain perceptions were significantly reduced under both LLM and MLM conditions. Pain decrease was more important under MLM condition than LLM condition at 2.20 min with a mean difference between both conditions of 9.7 (±3.9) ( p = 0.0195) and at 11.30 min [9.2 (±3.3), p = 0.0099]. LLM is correlated with CPM but not MLM, suggesting different mechanisms between LLM and MLM. Musical intervention, a simple method of application, fits perfectly into a multidisciplinary global approach and helps to treat the pain and anxiety disorders of participants. Clinical trial registration: [ https://clinicaltrials.gov/ct2/show/NCT04862832 ], ClinicalTrials.gov [NCT04862832].
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.046 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".