A paradigm change to inform fibromyalgia research priorities by engaging patients and health care professionals
Bibliographic record
Abstract
Background: Research objectives should be focused toward advancing knowledge that has meaningful impact on health. However, research agendas are mostly driven by the health care community, with limited input from patients.Aims: In this study, prioirities of uncertainties for the management of fibromyalgia (FM) that could propel future research were identified by a defined process using the James Lind Alliance Priority Setting Partnership (JLA-PSP) methodology.Methods: As a first step, a survey was distributed across Canada that engaged patients, caregivers, and health care professionals to provide narrative input to eight open-ended questions regarding FM care. Responses were thematically condensed and synthesized into an initial list of 43 uncertainties used to guide a comprehensive literature search. Questions already effectively addressed in the literature were excluded, leaving 25 uncertainties that were ranked during a one-day consensus workshop.Results: Three broad themes emerged: the value of personalized targeted treatment and subgrouping of patients; the efficacy of various self-management strategies and educational initiatives; and identification of the ideal health care setting to provide FM care. Opioids and cannabinoids were the only specific pharmacologic interventions ranked as needing further research.Conclusions: The prioritized questions highlight the importance of recognizing the heterogeneity of FM symptoms, the need for a personalized treatment approach, and a better understanding of the value of self-management strategies. This is the first study that uses an established and transparent methodology to engage all FM stakeholders to help inform researchers and funding bodies of clinically relevant research priorities.
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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 | 0.486 | 0.251 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.019 | 0.057 |
| Scholarly communication | 0.036 | 0.045 |
| Open science | 0.011 | 0.036 |
| Research integrity | 0.017 | 0.040 |
| Insufficient payload (model declined to judge) | 0.005 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".