Doppler Signal and Bone Erosions at the Enthesis Are Independently Associated With Ultrasound Joint Erosive Damage in Psoriatic Arthritis
Bibliographic record
Abstract
Objective To explore the association of the Outcome Measures in Rheumatology ultrasound (US) entheseal abnormalities with the presence of US joint bone erosions in psoriatic arthritis (PsA). Methods Consecutive patients with PsA were included in this cross-sectional study. Demographic and clinical variables were collected. A bilateral US assessment was carried out at the following entheses: plantar fascia, and the quadriceps, patellar (proximal and distal), and Achilles tendons. The following US entheseal abnormalities were registered: hypoechogenicity, thickening, Doppler signal < 2 mm from the bony cortex, calcification/enthesophyte, and bone erosion. The presence of US joint bone erosions was investigated at the second and fifth metacarpophalangeal joints, ulnar head, and fifth metatarsophalangeal (MTP) joint, bilaterally, as well as at the level of the most inflamed joint on physical examination. Multiple linear regression analysis was performed to identify clinical and/or US variables associated with US-detected joint bone erosions. Results A total of 104 patients with PsA were enrolled. At least 1 joint bone erosion was found in 47 of 104 patients (45.2%). Bone erosions were most frequently detected at the fifth MTP joint level (42/208 joints [20.2 %] in 32/104 patients [30.8%]). In the multivariate model, only a power Doppler (PD) signal at the enthesis (P< 0.001, standardized β = 0.51), bone erosions at the enthesis (P= 0.02, standardized β = 0.20), PsA disease duration (P= 0.04, standardized β = 0.17), and greyscale joint synovitis (P= 0.03, standardized β = 0.42) were associated with US-detected joint bone erosions. Conclusion PD signal and bone erosions at the enthesis represent sonographic biomarkers of a more severe subset of PsA in terms of US-detected joint erosive damage.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".