Fibromyalgia Assessment Screening Tool: Clues to Fibromyalgia on a Multidimensional Health Assessment Questionnaire for Routine Care
Bibliographic record
Abstract
OBJECTIVE: To develop feasible indices as clues to comorbid fibromyalgia (FM) in routine care of patients with various rheumatic diseases based only on self-report multidimensional Health Assessment Questionnaire (MDHAQ) scores, which are informative in all rheumatic diagnoses studied. METHODS: All patients with all diagnoses complete an MDHAQ at each visit; the 2011 FM criteria questionnaire was added to the standard MDHAQ between February 2013 and August 2016. The proportion of patients who met 2011 FM criteria or had a clinical diagnosis of FM was calculated. Individual candidate MDHAQ measures were compared to 2011 FM criteria using receiver-operating characteristic (ROC) curves; cutpoints to recognize FM were selected from the area under the curve (AUC) for optimal tradeoff between sensitivity and specificity. Cumulative indices of 3 or 4 MDHAQ measures were analyzed as fibromyalgia assessment screening tools (FAST). RESULTS: In 148 patients, the highest AUC in ROC analyses versus 2011 FM criteria were seen for MDHAQ symptom checklist, self-report painful joint count, pain visual analog scale (VAS), and fatigue VAS. The optimal cutpoints were ≥ 16/60 for symptom checklist, ≥ 16/48 for self-report painful joint count, and ≥ 6/10 for both pain and fatigue VAS. Cumulative FAST indices of 2/3 or 3/4 MDHAQ measures correctly classified 89.4-91.7% of patients who met 2011 FM criteria. CONCLUSION: FAST3 and FAST4 cumulative indices from only MDHAQ scores correctly identify most patients who meet 2011 FM criteria. FAST indices can assist clinicians in routine care as clues to FM with a general rheumatology rather than FM-specific questionnaire.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".