Visualization for therapist-guided self-exercise system of Somatic Balance Restoring Therapy
Bibliographic record
Abstract
Chronic pain and general physical discomfort are common symptoms of a disorder, and yet contain important information on the state of the human body. This is especially true when it relates to disorders found within the human musculoskeletal system. Although clinical pain research is confounded by many complex factors, it is possible to overcome such obstacles by promoting a multidisciplinary and holistic approach that integrates traditional oriental techniques with Western medicine and science. As an assessment of such an integrated approach, this paper aims to realize a computerized visual-graphic representation of a therapist-guided technique called the Somatic Balance Restoration Therapy (SBRT). The SBRT is a simple but effective self-exercise therapy, with initial assistance from a trained instructor/therapist. This guidance is necessary for optimal safety and effective execution of the therapy's painless exercise motions. Based on therapy records of over ten thousand cases, one of the authors has established a systematic approach in identifying and diagnosing distortions/malfunctions, while the other author has developed a visualization algorithm of the SBRT. Both aspects will be represented in this article with some examples of successful therapy.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.024 | 0.004 |
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".