Ultrasound to identify systemic lupus erythematosus patients with musculoskeletal symptoms who respond best to therapy: the US Evaluation For mUsculoskeletal Lupus longitudinal multicentre study
Bibliographic record
Abstract
OBJECTIVES: To determine whether SLE patients with inflammatory joint symptoms and US synovitis/tenosyovitis achieve better clinical responses to glucocorticoids compared with patients with normal scans. Secondary objectives included identification of clinical features predicting US synovitis/tenosynovitis. METHODS: In a longitudinal multicentre study, SLE patients with physician-diagnosed inflammatory joint pain received intramuscular methylprednisolone 120 mg once. Clinical assessments, patient-reported outcomes and bilateral hand/wrist USs were collected at 0, 2 and 6 weeks. The primary outcome (determined via internal pilot) was the early morning stiffness visual analogue scale (EMS-VAS) at 2 weeks, adjusted for baseline, comparing patients with positive (greyscale ≥2 and/or power Doppler ≥1) and negative US. Post hoc analyses excluded FM. RESULTS: Of 133 patients, 78 had a positive US. Only 53 (68%) of these had one or more swollen joint. Of 66 patients with one or more swollen joint, 20% had a negative US. A positive US was associated with joint swelling, symmetrical small joint distribution and serology. The primary endpoint was not met: in the full analysis set (N = 133) there was no difference in baseline-adjusted EMS-VAS at week 2 [-7.7 mm (95% CI -19.0, 3.5); P = 0.178]. After excluding 32 patients with FM, response was significantly better in patients with a positive US at baseline [baseline-adjusted EMS-VAS at 2 weeks -12.1 mm (95% CI -22.2, -0.1); P = 0.049]. This difference was greater when adjusted for treatment [-12.8 mm (95% CI -22, -3); P = 0.007]. BILAG and SLEDAI responses were higher in US-positive patients. CONCLUSION: In SLE patients without FM, those with a positive US had a better clinical response to therapy. Imaging-detected synovitis/tenosynovitis may be considered to decide on therapy and enrich clinical trials.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.000 |
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".