Finding the Role of Anifrolumab in the New Paradigm of Systemic Lupus Erythematosus Treatment
Bibliographic record
Abstract
The article by Tani et al, “Anifrolumab in Refractory Systemic Lupus Erythematosus: A Real-World, Multicenter Study,”1 evaluates the efficacy of anifrolumab (ANI) in patients with systemic lupus erythematosus (SLE) who failed to respond to other treatments, including biologics. There are many unmet needs in the treatment of SLE. Despite recent advances, remission and low disease activity (LDA) rates remain suboptimal; therefore, some patients with SLE are at high risk of developing endstage renal disease, organ damage, drug-related toxicities—particularly related to the prolonged use of glucocorticoids (GCs)—and premature mortality.2-4 The introduction of biologics (belimumab and ANI) in the European Alliance of Associations for Rheumatology (EULAR) recommendations for the management of nonrenal SLE5 challenges the rheumatologic community to adopt a paradigm shift in how they approach treatment. Also, the management of lupus nephritis (LN) has witnessed important advances with the approval of belimumab and voclosporin, a new calcineurin inhibitor. Consequently, there is a gradual transition from the traditional induction-maintenance regimen to the early use of combination therapies for LN, including immunosuppressants and biologics for patients with moderate or even mild disease.5 However, tools that would allow us to make better therapeutic decisions based on precision medicine without risk of overtreatment, which exposes patients to greater risks and higher costs without sufficient clinical benefit, are still lacking. Other unanswered questions remain: What is the optimal profile of patients who require combination therapy from the outset? Which patients will develop flares? Can we foresee treatment refractoriness? Precise indicators for using combinations of immunosuppressants are still unavailable. Other important points to consider are the differences in access and costs of these treatments, which introduce inequities, particularly in some low- and middle-income countries. We wonder if we will be able to treat our patients adequately worldwide. Currently, due to the … Address correspondence to Dr. G.J. Pons-Estel, Centro Regional de Enfermedades Autoinmunes y Reumáticas, Grupo Oroño, Bv. Oroño 740, CP 2000, Rosario, Santa Fe, Argentina. Email: gponsestel{at}yahoo.com.
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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 | 0.016 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".