A Pilot Study of External <i>Qigong</i> Therapy for Patients with Fibromyalgia
Bibliographic record
Abstract
OBJECTIVES: Although qigong is an important part of Traditional Chinese medicine (TCM) based on a philosophy similar to acupuncture, few studies of qigong exist in the Western medicine literature. To evaluate qigong therapy as a modality in treating chronic pain conditions such as fibromyalgia syndrome (FMS), we report a pilot trial of 10 women with severe FMS who experienced significant improvement after external qigong therapy (EQT). DESIGN: Ten patients with FMS completed five to seven sessions of EQT over 3 weeks with pre- and posttreatment assessment and a 3-month follow-up. Each treatment lasted approximately 40 minutes. OUTCOME MEASURES: Tender point count (TPC) and Fibromyalgia Impact Questionnaire (FIQ) were the primary measures. McGill Pain Questionnaire (MPQ), Beck Depression Inventory (BDI), anxiety, and self-efficacy were the secondary outcomes. RESULTS: Subjects demonstrated improvement in functioning, pain, and other symptoms. The mean TPC was reduced from 136.6 to 59.5 after EQT treatment; mean MPQ decreased from 27.0 to 7.2; mean FIQ from 70.1 to 37.3; and mean BDI from 24.3 to 8.3 (all p < 0.01). Many subjects reported reductions in other FMS symptoms, and two reported they were completely symptom-free. Results from the 3-month follow-up indicated some slight rebound from the post-treatment measures, but still much better than those observed at baseline. CONCLUSIONS: Treatment with EQT resulting in complete recovery for some FMS patients suggests that TCM may be very effective for treating pain and the multiplicity of symptoms associated with FMS. Larger controlled trials of this promising intervention are urgently needed.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".