Ultrasound, magnetic resonance imaging and radiography of the finger joints in psoriatic arthritis patients
Bibliographic record
Abstract
OBJECTIVES: To report the discrepancies and agreements between US, MRI and radiography of the hand in PsA, and to compare the sensitivity and specificity of US and radiography to MRI as the gold standard imaging study in PsA. METHODS: All of the 100 prospectively recruited consecutive PsA patients underwent clinical assessment and concomitant radiographic, US and MRI studies of the MCP, PIP and DIP joints of one hand. Synovitis, flexor tenosynovitis, extensor paratenonitis, erosions and bone proliferations were identified and scored. All readers were blinded to clinical data, and agreement was calculated based on prevalence-adjusted bias-adjusted kappa (PABAK). RESULTS: The prevalence of synovitis, flexor tenosynovitis, extensor paratenonitis and erosions was similar for US and MRI, while that of bone proliferation was significantly increased in US and radiography compared with MRI (P < 0.001). The absolute agreement between US and MRI was good-to-very good for synovitis (85-96%, PABAK = 0.70-0.92), flexor tenosynovitis (93-98%, PABAK = 0.87-0.96) and extensor paratenonitis (95-98%, PABAK = 0.90-0.97). Agreement between US, MRI and radiography was 96-98% (PABAK = 0.92-0.97) for erosions and 71-93% (PABAK = 0.47-0.87) for bone proliferations. Sensitivity of US with MRI as gold standard was higher for synovitis (0.5-0.86) and extensor paratenonitis (0.63-0.85) than for flexor tenosynovitis (0.1-0.75), while the specificity was high for each pathology (0.89-0.98). CONCLUSION: There is very good agreement between US and MRI for the detection of inflammatory changes in finger joints in PsA. US, radiography and MRI have a good-to-very good agreement for destructive changes.
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".