Fibromyalgia and myofascial pain syndrome: Two sides of the same coin? A scoping review to determine the lexicon of the current diagnostic criteria
Bibliographic record
Abstract
OBJECTIVE: Consistent terminology to describe the diagnostic criteria for fibromyalgia (FM) and myofascial pain syndrome (MPS) is required to address the reported inadequacies in diagnosis. The present review investigated intervention studies in FM and MPS populations to determine the lexicon of the current diagnostic criteria used to identify chronic musculoskeletal pain patients. METHODS: Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we conducted a scoping review to review systematically the literature obtained from five scientific databases between 1997 and February 2017. Included studies consisted of intervention studies that involved symptomatic musculoskeletal pain patients, of any age or gender, presenting with FM or MPS. Included studies were evaluated for musculoskeletal condition and the diagnostic criteria used to identify patient conditions. Extraction of study criteria focused on whether diagnostic criteria were explicitly stated, the diagnostic criteria used, physical findings, symptomatic duration and the profession of the healthcare provider who confirmed diagnosis. RESULTS: We identified 493 interventions, of which 410 were related to FM and 83 to MPS. The lexicon of the diagnostic criteria used for MPS tended to be less consistent in comparison to FM criteria, with notable differences in all comparative categories. CONCLUSIONS: The current review identified inconsistencies associated with the lexicon of the diagnostic criteria used to diagnose FM and MPS, and showed that there is wide variability in the terminology currently being used. These findings may have important implications for future development of consistent criteria to diagnose FM and MPS patients accurately.
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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 | 0.085 | 0.325 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.008 |
| Bibliometrics | 0.030 | 0.036 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.011 | 0.017 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".