Improving the arthritis component of the SLEDAI-2K
Bibliographic record
Abstract
OBJECTIVE: To propose a new definition for SLEDAI arthritis informed by imaging. METHODS: We performed a planned secondary analysis of observational data from a multicentre study evaluating SLE patients with inflammatory joint pain (swelling not required) using various clinical instruments, laboratory tests and ultrasound. For SLEDAI arthritis, assessors (blinded to ultrasound) were asked which of the glossary terms for arthritis in any version of the SLEDAI drove their decision to score for arthritis. These definitions were tested against ultrasound and other clinical variables. ROC analysis was used to test optimal joint count thresholds. RESULTS: A total of 78/133 patients had arthritis on SLEDAI-2K. In 21/78, clinician-observed swelling was not a reason for scoring (tenderness: 16/21; reported swelling between visits: 4/21; both of these: 1/21). No patient was scored for warmth or erythema alone. In total, 57 (73.1%) patients were scored for SLEDAI arthritis due to observed swelling, 90% had abnormal ultrasound (PPV = 90%, 95%CI: 79, 94). Of 21 patients with SLEDAI arthritis without observed swelling, 48% had abnormal ultrasound (PPV: 48%, 95%CI: 31, 67). Patients with SLEDAI arthritis with swelling had higher ESR, physician MSK-VAS, patient early morning stiffness (EMS)-VAS and IgG compared with other patients. Optimal sensitivity and specificity for ultrasound synovitis was 1 swollen joint using Youden's criteria. CONCLUSIONS: Our data suggest that the definition of arthritis in SLEDAI be modified to: 'Inflammatory musculoskeletal pain (symmetrical small joint distribution), with one or more clinically swollen joints witnessed on examination, that is not explained by another arthropathy'.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.002 | 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".