Belimumab in Systemic Lupus Erythematosus — What Can Be Learned from Longterm Observational Studies?
Bibliographic record
Abstract
Most patients with systemic lupus erythematosus (SLE) do not have optimal disease control1. Despite the available SLE treatments of today, patients still have flares, and some low-grade disease activity can be seen in many patients when followed over time. As a consequence of both chronic inflammation and use of corticosteroids, irreversible organ damage such as osteoporosis and cardiovascular disease may occur. In fact, disease activity over time has a great influence on organ damage and other outcomes2,3, and it is obvious that sustained control of disease activity is desirable in SLE. One such possibility of achieving longterm disease control and prevention of flare is published in the current issue of The Journal 4, where data are presented from an observational study in patients with SLE treated with belimumab for 7 years. Tumor necrosis factor blockers and other biological therapies have been used in rheumatoid arthritis and spondyloarthropathies for many years now, and this has dramatically changed the rheumatologic landscape. Meanwhile, phase III placebo-controlled randomized clinical trials (RCT) of different biologic therapies in the treatment of SLE have been unsuccessful — until the BLISS studies. The BLISS-525 and BLISS-766 studies are RCT in which treatment with belimumab showed significant effects on disease activity in SLE, and these studies formed the … Address correspondence to Dr. Bengtsson. E-mail: Anders.Bengtsson{at}med.lu.se
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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 | 0.076 | 0.235 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.011 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".