Association between musculoskeletal sonographic features and response to treatment in patients with psoriatic arthritis
Bibliographic record
Abstract
OBJECTIVE: To investigate the association between musculoskeletal sonographic features and clinical features, as well as treatment outcomes, in patients with active psoriatic arthritis (PsA). METHODS: A prospective cohort study was conducted involving patients with active PsA. Disease activity was assessed clinically at baseline and 3-6 months after initiating therapy, with a Disease Activity Index for PsA (DAPSA) score calculated. A baseline ultrasound examination of 64 joints, 28 tendons and 16 entheses evaluated the following lesions: synovitis, peritenonitis, enthesitis, tenosynovitis, new bone formation and erosions. Total scores for each lesion and total inflammatory and structural scores were calculated. The association between baseline sonographic scores and treatment outcomes was assessed using Cox proportional hazards models (for drug persistence) and generalised estimating equation models for DAPSA change. RESULTS: A total of 135 treatment periods (107 patients) were analysed. Multivariable analysis showed that a greater reduction in DAPSA score at follow-up was associated with higher baseline synovitis (β -3.89), peritenonitis (β -3.93) and enthesitis structural scores (β -2.91). Additionally, the total inflammatory score independently predicted DAPSA change (β -5.23) regardless of the total structural damage score. Drug persistence was analysed in 105 treatment periods, revealing that a higher sonographic erosion score was associated with earlier drug discontinuation (adjusted HR 1.28, 95% CI 1.03 to 1.61). CONCLUSION: The study results provide preliminary evidence supporting the utility of musculoskeletal ultrasound in predicting treatment response and drug persistence in PsA.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".