Quantum Therapy: A New Way in Osteoporosis Primary Prevention and Treatment
Bibliographic record
Abstract
The paper highlights the role played by quantum therapy in Osteoporosis Primary Prevention and treatment. We provide an overview of other useful treatments in Osteoporosis prevention such as CoQ10, Melatonin and Mediterranean Diet comparing their efficacy to that offered by the quantum therapy. This is done through ‘Quantum Biophysical Semeiotics’ bed-side evaluation, monitoring the results and efficiency of ongoing therapies aimed at improving mitochondrial and endothelial function, when it is unpaired in any biological systems. 'Quantum Biophysical Semeiotics’ theory is an extension of medical semeiotics. It is grounded on a multidisciplinary approach that involves chemistry and biology, genetics and neuroscience, chaos theory and quantum physics. It is based on the method of ‘Auscultatory Percussion’, through which by means of the common stethoscope, it is possible to listen to the signs that the body gives us when appropriately stimulated. The stimuli are used to induce consistent behaviour -typical of dissipative systems far from equilibrium, according to Prigogine - in precise and well defined biological systems of the human body, thus giving local qualitative information on the state of health or disease, whether potential, being developed but not yet evident by usual clinical trial, effective or even in chronic phase. The 'Quantum Biophysical Semeiotics’ theory provides very detailed case studies based on the latency time, duration, and intensity of the reflexes, which play a central role in such a diagnostic method.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".