Fibromyalgia Syndrome Module at OMERACT 9: Domain Construct
Bibliographic record
Abstract
The objective of the module was to (1) establish a core domain set for fibromyalgia (FM) assessment in clinical trials and practice, (2) review outcome measure performance characteristics, (3) discuss development of a responder index for assessment of FM in clinical trials, (4) review objective markers, (5) review the domain of cognitive dysfunction, and (6) establish a research agenda for outcomes research. Presentations at the module included: (1) Results of univariate and multivariate analysis of 10 FM clinical trials of 4 drugs, mapping key domains identified in previous patient focus group: Delphi exercises and a clinician/researcher Delphi exercise, and breakout discussions to vote on possible essential domains and reliable measures; (2) Updates regarding outcome measure status; (3) Update on objective markers to measure FM disease state; and (4) Review of the issue of cognitive dysfunction (dyscognition) in FM. Consensus was reached as follows: (1) Greater than 70% of OMERACT participants agreed that pain, tenderness, fatigue, patient global, multidimensional function and sleep disturbance domains should be measured in all FM clinical trials; dyscognition and depression should be measured in some trials; and stiffness, anxiety, functional imaging, and cerebrospinal fluid biomarkers were identified as domains of research interest. (2) FM domain outcome measures have generally proven to be reliable, discriminative, and feasible. More sophisticated and comprehensive measures are in development, as is a responder index for FM. (3) Increasing numbers of objective markers are being developed for FM assessment. (4) Cognitive dysfunction assessment by self-assessed and applied outcome measures is being developed. In conclusion, a multidimensional symptom core set is proposed for evaluation of FM in clinical trials. Research on improved measures of single domains and composite measures is ongoing.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.006 |
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".