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].
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 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".