Sternoclavicular joint involvement in psoriatic arthritis: a hidden indicator of disease severity
Bibliographic record
Abstract
OBJECTIVES: Sternoclavicular joint (SCJ) involvement in PsA is poorly characterized. We aimed to assess its prevalence and clinical associations in a longitudinal PsA cohort. METHODS: We retrospectively analysed prospectively collected data from the Gladman Krembil Psoriatic Arthritis Program. Patients with SCJ involvement (tender and/or swollen SCJ) were identified, and their demographic, clinical, laboratory and treatment details were retrieved. Univariable and multivariable generalized estimating equation models were used to assess associations with clinically swollen SCJ. Results are reported as odds ratios (OR) with 95% CI. RESULTS: Among the 1737 patients, 182 (10.5%) had SCJ involvement, including 36 (2.1%) with clinically swollen SCJs ever over follow-up. SCJ involvement was present at enrolment into the cohort (baseline) in 33.1% of affected individuals. The mean age was 41.4 ± 12 years, and 42% were male. In univariable analyses, SCJ involvement was significantly associated with higher swollen joint count (SJC) (OR 1.142, 95% CI 1.08-1.16), dactylitis (4.51, 2.02-10.05) and enthesitis (6.74, 3.45-13.15). No significant associations were observed with sex, disease duration, axial and nail disease, PASI, pustular psoriasis or HLA markers. In multivariable analysis, SCJ swelling remained associated with higher SJC (1.08, 1.03-1.14), enthesitis (4.54, 1.98-10.45), dactylitis (4.54, 1.98-10.45) and lower biologic or targeted synthetic DMARD use (0.30, 0.10-0.88). CONCLUSION: SCJ involvement is an underrecognized but clinically meaningful manifestation of PsA, associated with greater disease burden and enthesitis, often requiring escalation to advanced therapies.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".