Ultrasound and Magnetic Resonance Imaging in the Evaluation of Psoriatic Dactylitis: Status and Perspectives
Bibliographic record
Abstract
OBJECTIVE: Dactylitis, a characteristic feature of the spondyloarthropathies, occurs in up to 48% of patients with psoriatic arthritis (PsA). No clear consensus on the underlying components and pathogenesis of dactylitis exists in the literature. We undertook a systematic review of ultrasound (US) and magnetic resonance imaging (MRI) literature to better define imaging elements that contribute to the dactylitic digit seen in PsA. Our objectives were to determine first the level of homogeneity of each imaging modality's definition of the components of dactylitis, and second, to evaluate the metric properties of each imaging modality according to the Outcome Measures in Rheumatology Clinical Trials (OMERACT) filter. METHODS: Searches were performed in PUBMED and EMBASE for articles pertaining to MRI, US, and dactylitis. Data regarding the reported features of dactylitis were collected and categorized, and the metrological qualities of the studies were assessed. RESULTS: The most commonly described features of dactylitis were flexor tendon tenosynovitis and joint synovitis (90%). Extratendinous soft tissue thickening and extensor tendonitis were described nearly equally as being present and absent. Discrepancy exists as to whether entheses proper contribute to the etiology of dactylitis. An increasing number of studies categorize abnormalities in several tissue compartments including the soft tissue, tendon sheaths, and joints, as well as ligaments. CONCLUSION: The understanding of which tissues contribute to dactylitic inflammation has evolved. However, there is a lack of literature regarding the natural history of these abnormalities. This systematic review provides guidance in defining elementary lesions that may discriminate dactylitic digits from normal digits, leading to development of a composite measure of activity and severity of dactylitis.
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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 | 0.015 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".