The Role of Ultrasound in Psoriatic Arthritis — Do We Need a Score?
Bibliographic record
Abstract
Enthesitis is one of the hallmarks of the spondyloarthritis (SpA) group, including psoriatic arthritis (PsA)1,2. Enthesitis is usually defined as inflammation at the site of attachment of a tendon, ligament, and capsule onto bone, which can cause significant pain and disability for patients2. In recent years, the interest in this domain of disease has grown with increasing evidence of its prevalence and potential importance in the pathogenesis of the disease3. One of the limitations in understanding the exact role of enthesitis in these diseases has been the difficulty in assessing this feature3,4. Usually enthesitis is evaluated by clinical assessment, which measured the pain provoked by physical examination of entheseal sites. However, tenderness at the entheseal site does not always denote inflammation, and its absence does not rule out enthesitis. The introduction of new drugs and the wider use of imaging, especially magnetic resonance imaging and ultrasound (US) in clinical and research practice, highlight the pivotal role of enthesitis for the diagnosis and management of both SpA (axial and peripheral) and PsA. Nevertheless, it has frequently been shown that clinical assessment of pain at tendon insertions does not always correlate with imaging assessment of inflammation5,6,7,8. The clinical examination of enthesitis may also identify pain from tendinosis, from nearby joint synovitis, or from other pain mechanisms without any true involvement of the adjacent enthesis. In addition, it has been reported that a significant proportion of patients with PsA, and indeed patients with psoriasis, have subclinical enthesitis with inflammation seen on imaging, without tenderness at the insertion9. US has been proven to be a valuable tool to assess entheseal involvement across the SpA spectrum, including PsA7,8,9 … Address correspondence to L.C. Coates, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, Windmill Road, Oxford OX3 7LD, UK. E-mail: laura.coates{at}ndorms.ox.ac.uk.
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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 | 0.014 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".