What Does It Mean to Be a British Isles Lupus Assessment Group–Based Composite Lupus Assessment Responder? Post Hoc Analysis of Two Phase III Trials
Bibliographic record
Abstract
OBJECTIVE: The British Isles Lupus Assessment Group-based Composite Lupus Assessment (BICLA) is a validated global measure of treatment response in systemic lupus erythematosus (SLE) clinical trials. To understand the relevance of BICLA in clinical practice, we investigated relationships between BICLA response and routine SLE assessments, patient-reported outcomes (PROs), and medical resource utilization. METHODS: This was a post hoc analysis of pooled data from the phase III, randomized, placebo-controlled, 52-week TULIP-1 (ClinicalTrials.gov identifier: NCT02446912; n = 457) and TULIP-2 (ClinicalTrials.gov identifier: NCT02446899; n = 362) trials of intravenous anifrolumab (150/300 mg once every 4 weeks) in patients with moderate-to-severe SLE. Changes from baseline to week 52 in clinical assessments, PROs, and medical resource use were compared in BICLA responders versus nonresponders, regardless of treatment assignment. RESULTS: BICLA responders (n = 318) achieved significantly improved outcomes compared with nonresponders (n = 501), including lower flare rates, higher rates of attainment of sustained oral glucocorticoid taper to ≤7.5 mg/day, greater improvements in PROs (Functional Assessment of Chronic Illness Therapy-Fatigue, Short Form 36 Health Survey), and fewer SLE-related hospitalizations/emergency department visits (all nominal P < 0.001). Compared with nonresponders, BICLA responders had greater improvements in global and organ-specific disease activity (Physician's Global Assessment, SLE Disease Activity Index 2000, Cutaneous Lupus Erythematosus Disease Area and Severity Index Activity, and joint counts; all nominal P < 0.001). BICLA responders had fewer lupus-related serious adverse events than nonresponders. CONCLUSION: BICLA response is associated with clinical benefit in SLE assessments, PROs, and medical resource utilization, confirming its value as a clinical trial end point that is associated with measures important to patient care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.059 | 0.070 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.014 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".