Content and Criterion Validity of the Preliminary Core Dataset for Clinical Trials in Fibromyalgia Syndrome
Bibliographic record
Abstract
OBJECTIVE: Increasing research interest and emerging new therapies for treatment of fibromyalgia (FM) have led to a need to develop a consensus on a core set of outcome measures that should be assessed and reported in all clinical trials, to facilitate interpretation of the data and understanding of the disease. This aligns with the key objective of the Outcome Measures in Rheumatology (OMERACT) initiative to improve outcome measurement through a data driven, interactive consensus process. METHODS: Through patient focus groups and Delphi processes, working groups at previous OMERACT meetings identified potential domains to be included in the core data set. A systematic review has shown that instruments measuring these domains are available and are at least moderately sensitive to change. Most instruments have been validated in multiple languages. This pooled analysis study aims to develop the core data set by analyzing data from 10 randomized controlled trials (RCT) in FM. RESULTS: Results from this study provide support for the inclusion of the following in the core data set: pain, tenderness, fatigue, sleep, patient global assessment, and multidimensional function/health related quality of life. Construct validity was demonstrated with outcome instruments showing convergent and divergent validity. Content and criterion validity were confirmed by multivariate analysis showing R square values between 0.4 and 0.6. Low R square value is associated with studies in which one or more domains were not assessed. CONCLUSION: The core data set was supported by high consensus among attendees at OMERACT 9. Establishing an international standard for RCT in FM should facilitate future metaanalyses and indirect comparisons.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.501 | 0.748 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.010 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".