Explaining a diagnosis of fibromyalgia in primary care: a scoping review
Bibliographic record
Abstract
BACKGROUND: Fibromyalgia is a common cause of chronic pain in the UK, with a huge individual and societal impact. Despite this, it remains difficult to diagnose and treat. The explanation of a fibromyalgia diagnosis can lead to difficult therapeutic relationships, with attitudinal issues and negative profiling of patients. This can lead to frustration, and have a harmful impact on health outcomes. AIM: To review how an explanation of a fibromyalgia diagnosis is provided in primary care in order to establish a model of best practice when educating patients on their diagnosis. DESIGN & SETTING: Scoping review of articles written in English. METHOD: MEDLINE, Embase, Web of Science, and grey literature were searched. Articles were extracted, reviewed, and analysed according to the inclusion criteria. RESULTS: In total, 29 records met the inclusion criteria. The following six overarching themes were identified: patient education; physician education; importance of the multidisciplinary team; importance of patient-centred care; the value of primary care; and useful resources. The literature illustrated that describing fibromyalgia using analogies to illustrate the pain sensitisation process can help patients understand their diagnosis better. This improves their willingness to accept management plans, particularly engagement with non-pharmacological therapies, which the literature suggested are best delivered within a multidisciplinary team. CONCLUSION: Key aspects of fibromyalgia should be explained to patients in order for them to gain a better understanding of their diagnosis. A 'one-size-fits-all' model for explaining the fibromyalgia diagnosis to patients is inappropriate because patients' experiences are individualised. Further research is required on whether different explanations impact patient outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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 teacher head, 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".