Possible Mechanisms for the Effects of Sound Vibration on Human Health
Bibliographic record
Abstract
This paper presents a narrative review of research literature to "map the landscape" of the mechanisms of the effect of sound vibration on humans including the physiological, neurological, and biochemical. It begins by narrowing music to sound and sound to vibration. The focus is on low frequency sound (up to 250 Hz) including infrasound (1-16 Hz). Types of application are described and include whole body vibration, vibroacoustics, and focal applications of vibration. Literature on mechanisms of response to vibration is categorized into hemodynamic, neurological, and musculoskeletal. Basic mechanisms of hemodynamic effects including stimulation of endothelial cells and vibropercussion; of neurological effects including protein kinases activation, nerve stimulation with a specific look at vibratory analgesia, and oscillatory coherence; of musculoskeletal effects including muscle stretch reflex, bone cell progenitor fate, vibration effects on bone ossification and resorption, and anabolic effects on spine and intervertebral discs. In every category research on clinical applications are described. The conclusion points to the complexity of the field of vibrational medicine and calls for specific comparative research on type of vibration delivery, amount of body or surface being stimulated, effect of specific frequencies and intensities to specific mechanisms, and to greater interdisciplinary cooperation and focus.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".