A Novel approach towards Neuro Acoustic Loops and their intervention in various Neuro Psychiatric Disorders
Bibliographic record
Abstract
Abstract Acoustic signals are not transmitted to the brain wholly by contrivances of the ear but influences us through the mediums of skin, bones, and viscera. The ear is awfully subtle to vibrations in the air and conveys the tessellations of these vibrations in a form that the brain distinguishes as sound and speech. The medical devices are being developed that consume high-intensity focused acoustic signals as a non-invasive method for diagnostics and treatment of various psychiatric ailments. The major benefit of these sound or music & acoustic signals that offers the technique so readily to use in non-invasive therapy is its ability to breach deep into the human body and supply to a specific site thermal or mechanical energy with sub-millimetre accuracy. In this paper an attempt is made to study the intervention of these sound frequencies in general and their effects on neuro-psychiatric mechanisms and allied phenomenon. The quantification of specific frequencies (Acoustic loops) which will ameliorate the various disorders after their identification can also be correlated with various biochemical parameters that will be analysed with regards to the changes effected by these interventions. The proposed research work might provide a novel approach of treatment for neuro psychiatric disorders using the above stated measures using acoustic loops intervention that uses specific spectral frequencies to trigger effective and discernible changes in the subjects/patients suffering from neuropsychiatric ailments etc. The work intends to provide the effect of the above mentioned on 50 subjects which are taken in isolation wherein the results are quite demanding.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".