Clinical trial parameters that influence outcomes in lupus trials that use the systemic lupus erythematosus responder index
Bibliographic record
Abstract
Objective: The SLE Responder Index (SRI) is a composite endpoint used in SLE trials. This investigation examined the clinical trial elements that drive response measured by the SRI. Methods: Analyses are based on data from two phase 3 trials (n = 2262) that evaluated the impact of an anti-B-cell activating factor antibody on disease activity using SRI-5 as the primary endpoint (ClinicalTrials.gov NCT01196091 and NCT01205438). Results: The SRI-5 response rate at week 52 for all patients was 32.8%. Non-response due to a lack of SLEDAI improvement, concomitant medication non-compliance or dropout was 31, 16.5 and 19.1%, respectively. Non-response due to deterioration in BILAG or Physician's Global Assessment after SLEDAI improvement, concomitant medication compliance and trial completion was 0.5%. Disease activity in three SLEDAI organ systems was highly prevalent at baseline: mucocutaneous, 90.6%; musculoskeletal, 82.9%; and immunologic, 71.6%. Disease activity in each of the other organ systems was <11% of patients. Four clinical manifestations were highly prevalent at baseline: arthritis, 82.6%; rash, 69.2%; alopecia, 58.2%; and mucosal ulcer, 32.5%. The combined prevalence of renal, vascular and CNS disease at baseline was 17.6%; these patients had high SRI-5 response rates. Adjustments to corticosteroids were allowed during the first 24 weeks. Increases in corticosteroids above 2.5 mg/day were observed in 16.2% of placebo patients over the first 24 weeks after randomization. Conclusion: The primary drivers of SRI-5 response were SLEDAI improvement, concomitant medication adherence and trial completion. Arthritis, rash, alopecia and mucosal ulcer were the most prevalent clinical manifestations at baseline. Corticosteroid increases and rare, highly weighted disease manifestations in SLEDAI can confound the SRI signal.
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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 | 0.344 | 0.445 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".