Assessment of enthesitis in patients with psoriatic arthritis using clinical examination and ultrasound
Bibliographic record
Abstract
BACKGROUND: Enthesitis is a major feature of psoriatic arthritis. However, clinical assessment of enthesitis is known to lack accuracy and have poor interobserver reliability. OBJECTIVE: To determine effect of training on clinical assessment of enthesitis and to compare ultrasonography with clinical examination for the detection of entheseal abnormalities. METHODS: 20 rheumatologists performed repeated assessment of enthesitis in patients with established psoriatic arthritis before and after a 2-hour training session in standardised enthesitis count according to Leeds Enthesitis Index (LEI) and Spondyloarthritis Research Consortium of Canada Enthesitis Index (SPARCC). Moreover, 20 patients underwent clinical and ultrasonographic examination of entheses to evaluate consensus-based elementary lesions of enthesitis. RESULTS: Training significantly increased Intra-class Correlation Coefficient for LEI from 0.18 to 0.82 and for SPARCC from 0.38 to 0.67. Ultrasound examination showed high associations between hypoechogenicity and increased thickness of the entheses and clinical examination. There was no correlation between erosions and enthesophytes found by ultrasound and clinical assessments. CONCLUSION: Training in standardised enthesitis scoring systems significantly improved clinical assessments of enthesitis and should be performed before use in daily clinical practice. Ultrasound revealed more advanced stages of enthesitis, such as enthesophytes and erosions, which were not detected with clinical examination.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".