Validation of the Greek Version of the Fibromyalgia Rapid Screening Tool
Bibliographic record
Abstract
BACKGROUND AND AIM: The Fibromyalgia Rapid Screening Tool (FiRST) is a brief, simple, and straightforward self-administered questionnaire that was developed by Perrot et al. for the detection of fibromyalgia syndrome in patients with diffuse chronic pain. The aim of our study was to develop and validate the Greek version of FiRST. METHODS: The study was set up as a prospective observational study. The original French version of FiRST was adapted into Greek using forward and backward translation. Patients with chronic diffuse pain with a clinical diagnosis of fibromyalgia and osteoarthritis based on the criteria of the American College of Rheumatology were invited to participate to the study. RESULTS: Of the 101 patients who met our inclusion criteria, 42 were diagnosed with fibromyalgia and 59 with osteoarthritis. The 2 groups did not differ significantly regarding gender and pain characteristics (duration, intensity). Cronbach's alpha coefficient was 0.79. Receiver operating characteristic analysis showed an area under the curve of 89% (95% confidence interval = 83 to 95%; SE: 0.032, P < 0.001). At a cutoff score of ≥ 5, FiRST showed a sensitivity of 86%, a specificity of 83%, a positive predictive value of 78%, and a negative predictive value of 89%. The intraclass coefficient for the test-retest reliability was 0.96. CONCLUSION: The Greek version of FiRST is a valid screening tool for fibromyalgia in daily practice.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".