Ultrasound to identify lupus patients with inflammatory joint symptoms with a better response to therapy: The USEFUL longitudinal multicentre study
Bibliographic record
Abstract
Abstract Objective To determine whether SLE patients with inflammatory joint symptoms and ultrasound-synovitis achieve better clinical responses to glucocorticoid compared to patients with normal scans. Secondary objectives included identification of clinical features predicting ultrasound-synovitis. Methods A longitudinal muticentre study of SLE patients with physician-diagnosed inflammatory joint pain was undertaken. Clinical assessments, patient-reported outcomes, and bilateral hands and wrist ultrasound were collected at 0-, 2- and 6-weeks after intramuscular methylprednisolone 120mg. The primary outcome (determined via internal pilot analysis) was EMS-VAS at 2-weeks, adjusted for the baseline value, comparing patients with positive (GS≥2 and/or PD≥1) and negative ultrasound. Post-hoc analyses adjusting for fibromyalgia were performed. Results Of 133 patients recruited, 78/133 had positive ultrasound, but only 68% of these had ≥1 swollen joint. Of 66/133 patients with ≥1 swollen joint, 20% had negative ultrasound. Positive ultrasound was associated with joint swelling, symmetrical small joint distribution and serology. In full analysis set (n=133) there was no difference in baseline-adjusted EMS-VAS at week 2 (−7.7mm 95% CI − 19.0mm, 3.5mm, p=0.178). After excluding 32 fibromyalgia patients, response was significantly better in patients with positive ultrasound at baseline (baseline-adjusted EMS-VAS at 2-weeks - 12.1 mm, 95% CI −22.2mm, −0.1mm, p=0.049). This difference was greater when adjusted for treatment (−12.8mm (95% CI −22mm, −3mm), p=0.007). BILAG and SLEDAI responses were higher in ultrasound-positive patients. Conclusions In SLE patients without fibromyalgia, those with positive ultrasound had a better clinical response to therapy. Imaging-detected synovitis should be used to select SLE patients for 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 | 0.006 |
| 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 | 0.000 | 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".