A systems approach to a therapist-guided exercise for restoring musculoskeletal balance
Bibliographic record
Abstract
The Human Musculoskeletal System (HMS) is a typical complex system with significant Degrees-of-Freedom (DOF) that can be described by a large number of variables. Chronic pain and general physical discomfort are common symptoms and yet contain important information on the state of the human body, especially when it relates to disorders found within the HMS. Since this system is highly complex and large in scale, clinical pain research has been confounded by many complex factors. It is possible to overcome these obstacles however by applying a multidisciplinary approach, which includes systems engineering, traditional oriental techniques, Western medicine and science. To assess such an integrated approach, this paper presents a therapist-guided exercise for restoring human musculoskeletal balance called the Somatic Balance Restoration Therapy (SBRT). With initial assistance from a trained therapist/instructor, the SBRT is a simple but effective self-exercise therapy. This guidance is necessary to ensure safety and the effective execution of this painless therapy. Based on therapy records with over ten thousand cases, one of the authors has established a systematic approach for identifying and diagnosing distortions and malfunctions within the HMS; while the other author has developed a computer algorithm of the SBRT. Both are integrated by a Systems Approach and will be demonstrated by successful therapy examples.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".