Do lupus disease activity measures detect clinically important change?
Bibliographic record
Abstract
OBJECTIVE: New scales for the clinical assessment of patients with systemic lupus erythematosus (SLE) are valid and reliable, and quantitate disease activity. We assessed the responsiveness to change of 2 widely used standardized multi-item lupus activity measures, the revised Systemic Lupus Activity Measure (SLAM-R) and the Systemic Lupus Erythematosus Disease Activity Index (SLEDAI), and their ability to detect clinically relevant changes. METHODS: Ninety-six (96) patients with definite SLE participated in this study. The group mean age was 45.0 (13.7) years, 91% were female, and the mean disease duration was 14.9 (7.5) years. Sociodemographic information, lupus activity (SLAM-R, SLEDAI), and damage were recorded at baseline. At each of the 5 monthly followup visits, the activity measures were repeated and a transition scale asked the physician if their patient's lupus activity had changed. Five different methods were used to compare the responsiveness of the activity measures studied: 1. the effect size; 2. the standardized response mean; 3. the control standardized response mean; 4. the area under the curve of a receiver operating characteristic (ROC) curve; and 5. a new multiple response modeling approach. RESULTS: Both SLAM-R and SLEDAI are responsive. SLAM-R is consistently, although moderately, more responsive than SLEDAI. All 5 methods of evaluating responsiveness yielded a consistent ranking of disease activity measures. CONCLUSION: SLAM-R and SLEDAI are responsive measures of lupus activity. SLAM-R appears to be more responsive than SLEDAI.
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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 | 0.035 | 0.175 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".