The Pattern of Musculoskeletal Complaints in Patients With Suspected Psoriatic Arthritis and Their Correlation With Physical Examination and Ultrasound
Bibliographic record
Abstract
OBJECTIVE: To describe the pattern of musculoskeletal (MSK) symptoms and their correlation with clinical and sonographic findings among psoriasis patients with suspected psoriatic arthritis (PsA). METHODS: Patients with psoriasis and no prior diagnosis of PsA were referred for assessment of their MSK complaints. The study included the following steps: (1) assessment by an advanced practice physiotherapist, (2) targeted MSK ultrasound, and (3) assessment by a rheumatologist. In addition, patients were asked to complete questionnaires about the nature and duration of their MSK symptoms and to mark the location of their painful joints on a homunculus. Each patient was classified by a rheumatologist as "Not PsA," "Possible PsA," or "PsA". MSK symptoms and patient-reported outcomes (PRO) were compared between patients with PsA and Possible/Not PsA. Agreement between modalities was assessed using κ statistics. RESULTS: = 0.02). There was no difference between the 2 groups in the presence, distribution, and duration of MSK symptoms. Analysis of agreement in physical examination between modalities revealed the strongest agreement between the rheumatologist and physiotherapist (κ = 0.28). The lowest levels of agreement were found between ultrasound and patient (κ = 0.08) and physiotherapist and ultrasound (κ = 0.08). CONCLUSION: The results of this study suggest that the intensity, rather than the type, duration, or distribution of MSK symptoms, is associated with PsA among patients with psoriasis.
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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 | 0.007 |
| 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 | 0.001 |
| 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".