Imaging Techniques: Options for the Diagnosis and Monitoring of Treatment of Enthesitis in Psoriatic Arthritis
Bibliographic record
Abstract
Psoriatic arthritis (PsA) affects up to 30% of patients with psoriasis and may include musculoskeletal manifestations such as enthesitis. Enthesitis is associated with joint damage, and early detection and treatment are essential to management of the disease. Traditionally assessed by clinical examination and conventional radiography, entheseal inflammation can now be more accurately assessed earlier in the disease using techniques such as ultrasound, magnetic resonance imaging, computed tomography, and molecular imaging. However, there is little consensus on the optimum definition for diagnosing enthesitis in PsA or on the ideal scoring system for measuring response to treatment. This review aims to summarize the benefits and limitations of different imaging modalities in the assessment of enthesitis. It also proposes that adoption of standardized definitions and validation of scoring systems and imaging techniques in clinical trials will allow the efficacy of new treatment options to be assessed more accurately.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".