Musculoskeletal Ultrasound in Systemic Lupus Erythematosus: Systematic Literature Review by the Lupus Task Force of the OMERACT Ultrasound Working Group
Bibliographic record
Abstract
OBJECTIVE: To identify and synthesize the best available evidence on the application of musculoskeletal (MSK) ultrasound (US) in patients with systemic lupus erythematosus (SLE) and to present the measurement properties of US in different elementary lesions and pathologies. METHODS: A systematic literature search of PubMed, Embase, and the Cochrane Library was performed. Original articles were included that were published in English between August 1, 2014, and December 31, 2018, reporting US, Doppler, synovitis, joint effusion, bone erosion, tenosynovitis, and enthesitis in patients with SLE. Data extraction focused on the definition and quantification of US-detected synovitis, joint effusion, bone erosion, tenosynovitis, enthesitis, and the measurement properties of US according to the OMERACT Filter 2.1 instruments selection. RESULTS: Of the 143 identified articles, 15 were included. Most articles were cross-sectional studies (14/15, 93%). The majority of the studies used the OMERACT definitions for ultrasonographic pathology. Regarding the measurement properties of US in different elementary lesions and pathologies, all studies dealt with face validity, content validity, and feasibility. Most studies achieved construct validity. Concerning the reliability of image reading, 1 study (1/15, 7%) assessed both intraobserver and interobserver reliability. For image acquisition, 4 studies (4/15, 27%) evaluated interobserver reliability and none had evaluated intraobserver reliability. Criterion validity was assessed in 1 study (1/15, 7%). Responsiveness was not considered in any of the studies. CONCLUSION: This literature review demonstrates the need for further research and validation work to define the involvement of US as an outcome measurement instrument for the MSK manifestations 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 | 0.011 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.008 |
| Bibliometrics | 0.021 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".