Development of a Preliminary Ultrasonographic Enthesitis Score in Psoriatic Arthritis — GRAPPA Ultrasound Working Group
Bibliographic record
Abstract
OBJECTIVE: To assess the performance of various sonographic elemental entheseal lesions in distinguishing between psoriatic arthritis (PsA) and controls to inform the development of a novel sonographic enthesitis score for PsA. METHODS: A total of 100 age- and sex-matched individuals (50 PsA and 50 controls) were evaluated. Eleven entheseal sites were scanned bilaterally according to a standardized protocol by 2 sonographers. Based on the Outcome Measures in Rheumatology (OMERACT) definition of sonographic enthesitis, the following lesions were assessed: structural entheseal changes (hypoechogenicity), thickening, bone erosion, enthesophytes, calcification, and Doppler signal, in addition to bursitis and bone irregularities. The images were read by 2 readers blinded to the clinical information. A series of logistic regression models were used to find the optimal combination of entheseal sites and elementary lesions that distinguished PsA from controls. RESULTS: Mean age was 55 ± 10 years (59% males). The optimal model that distinguished PsA from controls included 5 elementary lesions (enthesophytes, Doppler signal, erosions, thickening, and hypoechogenicity) and 6 entheseal sites (patellar ligament insertions into the distal patella and tibial tuberosity, Achilles tendon and plantar fascia insertions into the calcaneus, common extensor tendon insertion into lateral epicondyle, and supraspinatus insertion into the superior facet of the humerus). The area under the receiver-operating characteristic curve for this model was 0.93 (95% CI 0.88-0.98). CONCLUSION: We identified potential elemental ultrasonographic abnormalities and entheseal sites that could distinguish PsA and controls. This information will contribute to the development of a new sonographic score for assessment of enthesitis in patients with 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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".