Experimental muscle pain and music, do they interact?
Bibliographic record
Abstract
BACKGROUND: Music is used to evoke audio analgesia during dental procedures, but it is unknown if experimental pain and music interact. This study aimed to explore the multisensory interaction between contrasting types of music and experimentally induced muscle pain. METHODS: In 20 healthy women, 0.3 mL sterile hypertonic saline (5.8%) was injected into the masseter muscle during three sessions while contrasting music (classical and black metal) or no music was played in the background. Pain intensity was assessed every 15 seconds with a 0-100 mm visual analogue scale (VAS) until pain subsided. Pain spread (pain drawings), unpleasantness (VAS), anxiety (VAS), and pain quality (McGill Questionnaire) were assessed after the last pain assessment. RESULTS: Pain of high intensity was evoked at all sessions with a median (interquartile range) peak pain intensity of 78 (30) in the black metal music, 86 (39) in the classical music, and 77 (30) in the control session. The pain duration was 142 (150) seconds in the black metal music, 135 (150) seconds in the classical music, and 135 (172) seconds in the control session. The corresponding pain-drawing areas were 42 (52), 37 (36), and 44 (34), arbitrary units respectively. There were no differences in any of these variables (Friedman's test; P´s > .368), or in unpleasantness, anxiety, or pain quality between sessions (P´s > .095). CONCLUSIONS: Experimentally induced muscle pain does not seem to be influenced by contrasting types of background music. Further studies exploring the multisensory integration between music and experimental muscle pain are needed.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".