Framework for implementing treat-to-target in systemic lupus erythematosus routine clinical care: consensus statements from an international task force
Bibliographic record
Abstract
Implementation of Treat-to-Target (T2T) in routine clinical practice remains low in systemic lupus erythematosus (SLE). Real-world data reveal excessive use of glucocorticoids (GCs) and frequently inadequate disease control. Here, an international task force convened to develop a consensus framework for implementing T2T in routine clinical care of adult patients with SLE. This T2T task force comprised an international panel of 22 physicians involved in the care of SLE and 3 lupus patient research partners. Following a scoping review and online discussions, during which definitions and instruments available for T2T in SLE were examined, the panel developed potential framework statements for implementing T2T in SLE, which were extensively discussed before being agreed upon by Delphi consensus. Additionally, the current challenges of implementing T2T in SLE and how future research may address these issues were analyzed. The framework comprises 5 overarching principles and 11 statements. Despite the absence of formal evidence that T2T offers superiority to conventional SLE management, T2T in SLE has been recommended for over a decade. This task force offers a framework for effectively implementing T2T in SLE from a real-life perspective, informing a wide range of physicians, including those outside the limited circle of lupus specialists.
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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 | 0.355 | 0.239 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.010 |
| Bibliometrics | 0.015 | 0.010 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.011 | 0.016 |
| Research integrity | 0.012 | 0.020 |
| Insufficient payload (model declined to judge) | 0.003 | 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".