The Burden of Enthesitis, Inflammatory Arthritis, and Tenosynovitis on Musculoskeletal Ultrasound in Systemic Lupus Erythematosus: A Scoping Review
Bibliographic record
Abstract
OBJECTIVE: The objective of this scoping review was to summarize the current literature on the burden of inflammatory arthritis (IA), tenosynovitis, and enthesitis detected on musculoskeletal ultrasound (MSUS) in adult patients with systemic lupus erythematosus (SLE). METHODS: A systematic literature search was performed using PubMed, Embase, and the Cochrane Library between January 1, 2015, and February 1, 2025, according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) extension for scoping reviews. To be included, studies on the adult SLE population must have reported on either synovitis, tenosynovitis, or enthesitis. Exclusion criteria were pediatric studies, rhupus-only studies, case series with < 5 patients, review articles, and conference abstracts. A preestablished standardized data collection sheet was used. RESULTS: The literature search found 24 articles that met inclusion criteria. Studies reported on IA (n = 20), tenosynovitis (n = 16), and enthesitis (n = 7). Most studies were cross-sectional (n = 20/24). Using criteria that were discriminatory for inflammatory enthesitis (power Doppler and bone erosion), inflammatory enthesitis was found in patients with SLE and was more common at the distal patellar, proximal patellar, and quadriceps enthesis. In the general SLE population, studies on IA and tenosynovitis found clinical arthritis in 18-38% of patients, MSUS synovitis in 33-57% of patients, subclinical MSUS arthritis in 9-21% of patients, and tenosynovitis in 8-38% of patients. MSUS findings correlated with SLE disease activity indices, such as SLE Disease Activity Index (SLEDAI) and British Isles Lupus Assessment Group (BILAG). CONCLUSION: Enthesitis may be a new musculoskeletal domain in SLE. MSUS can be useful in diagnosing tenosynovitis and subclinical IA in patients with SLE.
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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 | 0.017 | 0.059 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.024 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".