Significance of Clinical Evaluation of the Metacarpophalangeal Joint in Relation to Synovial/Bone Pathology in Rheumatoid and Psoriatic Arthritis Detected by Magnetic Resonance Imaging
Bibliographic record
Abstract
OBJECTIVE: Rheumatologists base many clinical decisions regarding the management of inflammatory joint diseases on joint counts performed at clinic. We investigated the reliability and accuracy of physically examining the metacarpophalangeal (MCP) joints to detect inflammatory synovitis using magnetic resonance imaging (MRI) as the gold standard. METHODS: MCP joints 2 to 5 in both hands of 5 patients with rheumatoid arthritis (RA) and 5 with psoriatic arthritis (PsA) were assessed by 5 independent examiners for joint-line swelling (visually and by palpation); joint-line tenderness by palpation (tender joint count, TJC) and stress pain; and by MRI (1.5 Tesla superconducting magnet). Interrater reliability was assessed using kappa statistics, and agreement between examination and corresponding MRI assessment was assessed by Fisher's exact tests (p < 0.05 considered statistically significant). RESULTS: Interrater agreement was highest for visual assessment of swelling (kappa = 0.55-0.63), slight-fair for assessment of swelling by palpation (kappa = 0.19-0.41), and moderate (kappa = 0.41-0.58) for assessment of joint tenderness. In patients with RA, TJC, stress pain, and visual swelling assessment were strongly associated with MRI evaluation of synovitis. Visual swelling assessment demonstrated high specificity (> 0.8) and positive predictive value (= 0.8). For PsA, significant associations exist between TJC and MRI synovitis scores (p < 0.01) and stress pain and MRI edema scores (p < 0.04). Assessment of swelling by palpation was not significantly associated with synovitis or edema as determined by MRI in RA or PsA (p = 0.54-1.0). CONCLUSION: In inflammatory arthritis, disease activity in MCP joints can be reliably assessed at the bedside by examining for joint-line tenderness (TJC) and visual inspection for swelling. Clinical assessment may have to be complemented by other methods for evaluating disease activity in the joint, such as MRI, particularly in patients with PsA.
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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 | 0.009 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".