Music interferes with expectation-induced pain modulation: a controlled cross-over experimental study
Bibliographic record
Abstract
Abstract Introduction: Music reduces pain and anxiety in various contexts, but the possible effect on pain anticipatory mechanisms remains unclear. Objectives: This study examines the effects of a standardized musical intervention (Music Care) on pain perception and on pain modulation induced by expectations of low or high pain. Methods: Healthy participants were tested in an experimental study using a crossover design involving the musical intervention counterbalanced with an active auditory control condition (audiobook) and a silent control condition. Pain perception was assessed using contact heat stimulation, and expectations were manipulated using prestimulus anticipatory cues signalling high or low pain. Results: Perceived pain intensity, measured using a visual analog scale, was decreased during the music intervention and the audiobook compared to silence (P's < 0.001). Music was more effective than the audiobook control, especially at the higher pain stimulation level (P < 0.001). Anticipatory cues modulated pain and anxiety in the expected direction across all conditions (P's < 0.001). Music and the audiobook produced comparable reduction in (1) expectation-induced (1a) hypoalgesic and (1b) hyperalgesic effects (all P's < 0.005) and in (2) pain anticipatory anxiety (all P's < 0.05). Overall, music was more effective than the active auditory control to reduce pain but both forms of auditory distraction partly blocked the modulatory effects of low and high pain expectations. Conclusion: This study highlights the multiplicity of processes contributing to music-induced analgesia and suggests that music may help improve pain management in the context of high pain expectation and anxiety. However, music may also interfere with pain-relieving strategies involving the induction of low pain expectations.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".