<i>Chaoyi Fanhuan Qigong</i> and Fibromyalgia: Methodological Issues and Two Case Reports
Bibliographic record
Abstract
BACKGROUND: Qigong, which has many forms, was recently described as "meditative movement," and represents a self-care technique that can contribute to improved health. There are challenges involved in research into qigong, including defining the amount of instruction required for threshold effects, and whether there is a relationship between amount of practice and outcomes. Recent clinical trials examining Chaoyi Fanhuan Qigong (CFQ) for fibromyalgia have used a standardized regimen of practice over an 8-week period. CASE REPORT: Between a pilot trial and a subsequent larger controlled trial, 2 individuals with fibromyalgia of over 20 years' duration undertook levels 1-4 CFQ training involving movements and meditation at a community-based event and then practiced regularly over a 1-year period. They subsequently both undertook further training, and consolidated their health gains. Both observed major reductions in pain, improvements in sleep, mood, emotions, food and other allergies, and consider their condition essentially resolved. They have ceased taking several medications and have resumed their lives. RESULTS: The information provided by these individuals could not be derived from a clinical trial, as it is unlikely people would commit to this amount of practice. CONCLUSIONS: The case study approach provides data with respect to extent of practice, perseverance and long-term outcomes, and provides valuable insight into the potential of this self-care practice.
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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 | 0.015 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.011 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".