Advances in the Evaluation of Peripheral Enthesitis by Magnetic Resonance Imaging in Patients With Psoriatic Arthritis
Bibliographic record
Abstract
Enthesitis is a key disease manifestation in patients with psoriatic arthritis (PsA) that considerably contributes to pain, lower physical function, and reduced quality of life. Clinical assessment of enthesitis lacks sensitivity and specificity, and therefore better methods are urgently needed. Magnetic resonance imaging (MRI) allows detailed assessment of the components of enthesitis, and consensus-based validated MRI scoring systems exist. These include the Outcome Measures in Rheumatology (OMERACT) Heel Enthesitis MRI Scoring System (HEMRIS) method, which assesses the entheses of the heel region in a detailed manner, and the OMERACT MRI Whole-Body Score for Inflammation in Peripheral Joints and Entheses (MRI-WIPE) method, which provides an overall assessment of the inflammatory burden in the peripheral entheses and joints in the entire body using whole-body MRI. At an MRI workshop at the Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA) 2022 meeting in Brooklyn, the MRI appearances of peripheral enthesitis were described, as were the scoring methods. The utility of MRI for improved assessment of enthesitis was demonstrated with examples of patient cases. Clinical trials in PsA that evaluate enthesitis by MRI as a key endpoint should include the presence of MRI enthesitis as an inclusion criterion, and apply validated MRI outcomes to assess the effect of therapeutics on enthesitis are recommended.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".