International, multidisciplinary Delphi consensus recommendations on non-pharmacological interventions for fibromyalgia
Bibliographic record
Abstract
OBJECTIVES: To develop evidence-based expert recommendations for non-pharmacological treatments for pain, fatigue, sleep problems, and depression in fibromyalgia. METHODS: An international, multidisciplinary Delphi exercise was conducted. Authors of EULAR and the Canadian Fibromyalgia Guidelines Group, members of the American Pain Society and clinicians with expertise in fibromyalgia were invited. Participants were asked to select non-pharmacological interventions that could be offered for specific fibromyalgia symptoms and to classify them as either core or adjunctive treatments. An evidence summary was provided to aid the decision making. Items receiving >70% votes were accepted, those receiving <30% votes were rejected and those obtaining 30-70% votes were recirculated for up to two additional rounds. RESULTS: Seventeen experts participated (Europe (n = 10), North America (n = 6), and Israel (n = 1)) in the Delphi exercise and completed all three rounds. Aerobic exercise, education, sleep hygiene and cognitive behavioural therapy were recommended as core treatments for all symptoms. Mind-body exercises were recommended as core interventions for pain, fatigue and sleep problems. Mindfulness was voted core treatment for depression, and adjunctive treatment for other symptoms. Other interventions, namely music, relaxation, hot bath, and local heat were voted as adjunctive treatments, varying between symptoms. CONCLUSIONS: This study provided evidence-based expert consensus recommendations on non-pharmacological treatments for fibromyalgia that may be used to individualise treatments in clinical practice targeting the diverse symptoms associated with fibromyalgia.
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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 | 0.332 | 0.332 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".