Minimal Clinically Important Difference in the Fibromyalgia Impact Questionnaire
Bibliographic record
Abstract
OBJECTIVE: The Fibromyalgia Impact Questionnaire (FIQ) is a disease-specific composite instrument that measures the effect of problems experienced by patients with fibromyalgia (FM). Utilization of the FIQ in measuring changes due to interventions in FM requires derivation of a clinically meaningful change for that instrument. Analyses were conducted to estimate the minimal clinically important difference (MCID), and to propose FIQ severity categories. METHODS: Data from 3 similarly designed, 3-month placebo-controlled, clinical treatment trials of pregabalin 300, 450, and 600 mg/day in patients with FM were modeled to estimate the change in the mean FIQ total and stiffness items corresponding to each category on the Patient Global Impression of Change. FIQ severity categories were modeled and determined using established pain severity cutpoints as an anchor. RESULTS: A total of 2228 patients, mean age 49 years, 93% women, with a mean baseline FIQ total score of 62 were treated in the 3 studies. Estimated MCID on a given measure were similar across the studies. In a pooled analysis the estimated MCID (95% confidence interval) was 14% (13; 15) and for FIQ stiffness it was 13% (12; 14). In the severity analysis a FIQ total score from 0 to <39 was found to represent a mild effect, >or= 39 to <59 a moderate effect, and >or=59 to 100 a severe effect. CONCLUSION: The analysis indicates that a 14% change in the FIQ total score is clinically relevant, and results of these analyses should enhance the clinical utility of the FIQ in research and 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 | 0.030 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".