Accuracy of Physical Examination to Detect Synovial and Extra-Synovial Pathologies in Psoriatic Arthritis in Comparison to Ultrasonography
Bibliographic record
Abstract
We aimed to explore the accuracy of physical examination (PE) to detect the synovial and extra-synovial pathologies in psoriatic arthritis (PsA) in comparison to ultrasonography (US). Twenty-nine PsA patients with hand pain were included in the study. A detailed PE of the hands was performed and US scans were performed for the joints, extensor and flexor tendons, and entheses of the second to fifth fingers of both hands. The agreement between PE and US findings was calculated. The strongest agreement for the joints was between "swollen joints" and power Doppler (PD) signals in the metacarpophalangeal (MCP) joints and grey scale synovitis in the proximal interphalangeal (PIP) joints. The agreement of tender entheses on PE and inflammation on US (hypoechogenicity, thickening, and/or PD signals) was poor for both extensor (Kappa = -0.027, Prevalence Adjusted and Bias Adjusted Kappa (PABAK) = 0.344) and flexor compartments (Kappa = 0.039, PABAK = 0.569). Similar to enthesitis, comparison of any PE and US findings showed a poor agreement at the extensor and flexor tendon regions (extensor: Kappa = 0.123, PABAK = 0.448, and flexor: Kappa = 0.171, PABAK = 0.431). Our study showed that there was a poor to fair agreement of PE and US findings of hands. US can add value when determining the source of pain in PsA in the small joints.
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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 | 0.006 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".