Music in pain treatment: society, medicine and neurosciences
Bibliographic record
Abstract
Pain treatment and prevention is a common problem for the clinic and social health of all the states. The challenge is to give the patients a more short-term admission and a more complete pain relief, searching for less invasive therapies. In USA, Canada and Australia and other countries music therapy is widely being used with successful results. Nurses or special therapists of clinic staff have the particular role to distribute the music treatment inside the hospitals and in nursing homes. There is all a wide literature which demonstrates how since many years the role of nursery has been revealed for the clinical research, especially about pain relief. Music therapy uses the sound/music elements inside the user/operator relationship in a systemic operation process with preventive, rehabilitative and therapeutic aims. The physics elements of sounds strike and modify the psycho-neuro-himmuno-endocrynologic system, which diseases and illness are an alteration of body homeostatic balance. Music benefits would be effective because of the harmonic resonance or ‘entrainment’ principle, from the physic of waves and sounds. Beyond the clinic cases description, there are a lot of basic scientific studies which give information about the analgesic effects of music, utilizing physiological parameters and statistics. There are cognitive and neuroscientific proofs which demonstrate the positive effects of music: music would be a distraction element from pain, because of the inhibition of pain from the inside; music enhances the endorphin release (endogenous opioid peptides) inside the body, which act against pain; music can give the patient a pain control feeling; ‘slow’ music can relax the cardiac frequency and the rhythm of breath.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".