The search for pain relief in people with chronic fatigue syndrome: A descriptive study
Bibliographic record
Abstract
The purpose of this study was to investigate the use and perceived benefit of complimentary and alternative medicine (CAM) and physiotherapy treatments tried by people with chronic fatigue syndrome (CFS) to ease painful symptoms. This study used a descriptive, cross-sectional design. People with CFS who experienced pain were recruited to this study. Participants were asked during a semistructured interview about the treatments they had tried to relieve their pain. Each interview was conducted in the home of the participant. Fifty participants were recruited, of which, 10 participants were severely disabled by CFS. Eighteen participants were trying different forms of CAM treatment for pain relief at the time of assessment. Three participants were currently receiving physiotherapy. Throughout the duration of their illness 45 participants reported trying 19 different CAM treatments in the search for pain relief. Acupuncture was reported to provide the most pain relief (n=16). Twenty-seven participants reported a total of 16 different interventions prescribed by their physiotherapist. The results of this study suggest some physiotherapy and CAM treatments may help people manage painful CFS symptoms. Future research should be directed to evaluating the effectiveness of interventions such as acupuncture or gentle soft tissue therapies to reduce pain in people with CFS.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".