A Qualitative Analysis of Patient Perspectives and Preferences in Lupus Management to Guide Lupus Guidelines Development
Bibliographic record
Abstract
OBJECTIVE: A patient-centered approach for chronic disease management, including systemic lupus erythematosus (SLE), aligns treatment with patients' values and preferences, leading to improved outcomes. This paper summarizes how patient experiences, perspectives, and priorities informed the American College of Rheumatology (ACR) 2024 Lupus Nephritis (LN) and 2025 SLE screening, treatment guidelines. METHODS: We completed a cross-sectional qualitative study using content analysis of two Patient Panel meetings for the ACR LN and SLE guidelines. Key themes were presented by Patient Panel representatives during Voting Panel Meetings along with evidence for each recommendation, to ensure comprehensive discussions and align treatment recommendations with patients' priorities and values. RESULTS: Nineteen people (90% women) with diagnoses of SLE and/or LN participated in the Patient Panels and 17 consented to use their feedback for analysis. Thematic analysis of their discussions revealed nine patient-reported key themes in three domains: (1) treatment and monitoring of LN and SLE: medication side effects, daily function, treatment goals, and monitoring and screening; (2) clinical communication: strategies to optimize communication and provider and structural impediments to effective communication; and (3) improving transparency and information sharing: clinical trial participation, and medical costs and insurance coverage. These themes were actively incorporated into discussions during the Voting Panels for the ACR LN and SLE guidelines. CONCLUSION: This work supported the integration of patient experiences in the clinical practice guideline development process and aligned recommendations with real-world patient experiences and priorities, thereby enhancing the clinical applicability of the ACR LN and SLE guidelines.
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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 | 0.028 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".