Evaluation of Magnetic Resonance Imaging Responsiveness in Active Psoriatic Arthritis at Multiple Timepoints during the First 12 Weeks of Antitumor Necrosis Factor Therapy
Bibliographic record
Abstract
OBJECTIVE: To assess the responsiveness of high- and low-field extremity magnetic resonance imaging (MRI) variables at multiple timepoints in the first 12 weeks post-antitumor necrosis factor (anti-TNF) therapy initiation in patients with psoriatic arthritis (PsA) and active dactylitis. METHODS: Twelve patients with active PsA and clinical evidence of dactylitis involving at least 1 digit were recruited. Patients underwent sequential high-field conventional (1.5 Tesla) and extremity low-field MRI (0.2 Tesla) of the affected hand or foot, pre- and postgadolinium at baseline (pre-TNF), 2 weeks (post-TNF), 6 weeks, and 12 weeks. A blinded observer scored all images on 2 occasions using the PsA MRI scoring system. RESULTS: Eleven patients completed the study, but only 6 patients completed all high-field and low-field MRI assessments. MRI scores demonstrated rapid response to TNF inhibition with score reduction in tenosynovitis, synovitis, and osteitis at 2 weeks. Intraobserver reliability was good to excellent for all variables. High-field MRI demonstrated greater sensitivity to tenosynovitis, synovitis, and osteitis and greater responsiveness to change posttreatment. Treatment responses were maintained to 12 weeks. CONCLUSION: This study demonstrates the use of MRI in detecting early response to biologic therapy. MRI variables of tenosynovitis, synovitis, and osteitis demonstrated responsiveness posttherapy with high-field scores more responsive to change than low-field scores.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".