2025 American College of Rheumatology ( <scp>ACR</scp> ) Guideline for the Treatment of Systemic Lupus Erythematosus
Bibliographic record
Abstract
OBJECTIVE: To provide evidence-based and expert guidance for the treatment and management of non-renal systemic lupus erythematosus (SLE); treatment and management of lupus nephritis are addressed in a separate guideline. METHODS: Clinical questions for treatment and management of SLE were developed in the PICO format (population, intervention, comparator, and outcome). Systematic literature reviews were developed for each PICO question, and the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) methodology was used to assess evidence quality and formulate recommendations. The Voting Panel achieved a consensus of ≥70% agreement on the direction (for or against) and strength (strong or conditional) of each recommendation. RESULTS: We present recommendations and ungraded, consensus-based good practice statements for the treatment and management of SLE that are applicable to pediatric and adult patients. Recommendations emphasize uniform treatment with hydroxychloroquine, limiting duration of glucocorticoid use, and early introduction of conventional and/or biologic immunosuppressive therapies to achieve and maintain control of SLE inflammation (remission or a low level of disease activity), reduce SLE-related morbidity and mortality, and minimize medication-related toxicities. CONCLUSION: This guideline presents direction regarding treatment and management of SLE and provides a foundation for well-informed, shared clinician-patient decision-making. These recommendations should not be used to limit or deny access to therapies, as treatment decisions may vary due to the unique clinical situation and personal preferences of each person with SLE.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.014 | 0.012 |
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".