External Validation of the Lupus Multivariable Outcome Score for Systemic Lupus Erythematosus Trials
Bibliographic record
Abstract
OBJECTIVE: Development of new systemic lupus erythematosus (SLE) treatments requires an effective responder index. Toward this end, we have recently developed a new Lupus Multivariable Outcome Score (LuMOS) to optimize discrimination between actively treated patients and those on placebo. We now report on external validation of LuMOS in two independent clinical trials. METHODS: Validation was performed with the Illuminate data sets that evaluated tabalumab (TB) in SLE. To accommodate laboratory results assessed on different platforms, we developed a standardized LuMOS 2.0 model that uses z score transformations of biomarker values. For validation, we calculated LuMOS 2.0 scores at week 52 for all participants. Effect size (ES), with 95% confidence intervals (CIs), compared the ability of LuMOS and the SLE Responder Index-5 (SRI-5) to discriminate between outcomes in patients randomized to TB dosage and outcomes in those randomized to a placebo. RESULTS: Mean LuMOS 2.0 scores were significantly higher (P < 0.0001) for the TB groups than the placebo group, including the Illuminate-1 trial, in which the SRI-5 did not identify significant treatment effects. For both TB groups in both trials, LuMOS 2.0-based ES indicated moderately strong treatment effects (>0.4) in contrast to weak SRI-5 effects (<0.25). For monthly TB, LuMOS 2.0-based ES were 0.44 (95% CI: 0.30-0.59) and 0.54 (95% CI: 0.39-0.68) for the Illuminate-1 and Illuminate-2 trials versus corresponding SRI-5-based ES of 0.13 (95% CI: -0.02 to +0.27) and 0.15 (95% CI: 0.01-0.30). CONCLUSION: LuMOS 2.0 detected significantly greater treatment effects compared with the SRI-5 in the Illuminate trials. Additional validation of LuMOS 2.0 in trials of non-B cell-directed therapies will be necessary to document its universality as an outcome measure.
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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 | 0.285 | 0.330 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".