Identification of the Clinical Features Distinguishing Psoriatic Arthritis and Fibromyalgia
Bibliographic record
Abstract
OBJECTIVE: To identify the clinical features that can help to distinguish between psoriatic arthritis (PsA) and fibromyalgia (FM). METHODS: Our cross-sectional study was carried out in 10 Italian rheumatology centers between January and September 2009, and enrolled all consecutive patients with PsA and FM who agreed to participate. Standard clinical and laboratory data for PsA and FM were collected from all patients. Records were made of somatic symptoms, response to nonsteroidal antiinflammatory drugs (NSAID), self-evaluated pain, general health, disability, and responses to the Fibromyalgia Impact Questionnaire. Data were statistically analyzed by univariate and multivariate analyses, and receiver-operating characteristic curves. The analysis concentrated on the clinical features shared by the 2 conditions. RESULTS: Two hundred sixty-six patients with PsA (mean age 51.7 yrs; disease duration 10.2 yrs) and 120 patients with FM (mean age 50.2 yrs; disease duration 5.6 yrs) were evaluated. Univariate analysis showed that patients with FM had higher mean tender point and enthesitis scores, more somatic symptoms, and responded less to NSAID. Multivariate analysis showed that the presence of ≥ 6 FM-associated symptoms and ≥ 8 tender points was the best predictor of FM. CONCLUSION: The shared clinical features of PsA and FM that had the greatest discriminating power for FM were the number of FM-associated symptoms and tender point count.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".