myoActivation: A Structured Process for Chronic Pain Resolution
Bibliographic record
Abstract
Chronic pain is a significant burden in all societies. The myofascial origins of chronic pain are often unrecognized but play a major role in chronic pain generation. Myofascial release has been shown to be effective and can augment the limited number of therapeutic tools available to manage chronic pain. However, there is no standardized approach that allows for comparative analysis of this technique. myoActivation® is a unique therapeutic system, which targets active myofascial trigger points, fascia in tension, and scars in patients with chronic pain. Targets for intervention are determined through obtaining a history of lifetime trauma and a structured, reproducible posture, and movement assessment. Catenated cycles of movement tests, palpation, and needling are used to achieve the goal of pain resolution through restoration of soft tissue integrity. This chapter describes the distinctive features of myoActivation from the important key elements of the patient’s clinical history, through to the aftercare instructions. Relevant evidence for each component will be presented. Case studies will be used to illustrate some important concepts and the effectiveness of myoActivation. This chapter is relevant to all clinicians that manage people living with chronic pain.
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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 | 0.001 |
| 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 | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.037 | 0.018 |
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".