Anifrolumab Study for Treatment Effectiveness in the Real World (ASTER) among patients with systemic lupus erythematosus: protocol for an international observational effectiveness study
Bibliographic record
Abstract
INTRODUCTION: Systemic lupus erythematosus (SLE) is a chronic autoimmune disease with a diverse clinical presentation that involves multiple organ systems and may lead to organ damage and increased risk of mortality. SLE is associated with a high burden of disease that can include loss of productivity and employment and reduced health-related quality of life. The current standard of care for SLE is primarily based on immunosuppression and glucocorticoids and is associated with risk of toxicities and poor tolerability. Anifrolumab, a human monoclonal antibody to type I interferon receptor subunit 1, was recently approved as a new treatment for patients with moderate-to-severe SLE. METHODS AND ANALYSIS: Here, we report the study design of the ongoing, multinational Anifrolumab Study for Treatment Effectiveness in the Real World (ASTER) that includes 3-years of follow-up beginning with the first infusion of anifrolumab and 1 year of retrospective baseline data. ASTER aims to enrol 500 adult patients receiving anifrolumab for SLE in Europe and Canada. The key study objective is to describe the real-world effectiveness of anifrolumab in routine clinical practice, including clinician-reported disease activity and patient-reported outcomes collected via mobile application. This mobile application also includes a medication diary, where patients report their prescription and non-prescription medication use for SLE on a weekly basis; these data will lend insights on treatment patterns for the study population. ETHICS AND DISSEMINATION: The design of the ASTER study was informed through consultations with patients with SLE who provided important insights to help maximise patient engagement, retention and the collection of key, patient-relevant endpoints. ASTER enrolment began in February 2023 and the study is expected to finish in 2029. TRIAL REGISTRATION NUMBER: NCT05637112.
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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 | 0.060 | 0.045 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.033 | 0.006 |
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".