Patient Perspectives on a Decision Aid for Systemic Lupus Erythematosus: Insights and Future Considerations
Bibliographic record
Abstract
OBJECTIVE: Systemic lupus erythematosus (SLE) is a chronic autoimmune disease with a wide spectrum of clinical manifestations. A decision aid (DA) for SLE was developed and implemented in 15 rheumatology clinics throughout the United States. This study explored the experiences of patients who viewed the DA to understand how patients engage with and respond to the SLE DA. METHODS: We conducted a qualitative descriptive study using semistructured interviews with a convenience sample of 24 patients during May to July 2022. RESULTS: Patients recognized the value of the SLE DA in providing general knowledge about SLE and different treatment options. However, patients expressed a desire for more comprehensive lifestyle information to better manage their condition. Another theme was the importance of having multiple formats available to cater to their different needs, as well as tailoring the DA to different stages of SLE. CONCLUSION: This study contributes to a broader understanding of how to provide patient-centered care for patients with SLE by offering practical insights that can inform the development of more effective, patient-centric health information technologies for managing chronic diseases, ultimately improving patient outcomes. Overall, this study underscores the significance of optimizing both the information content and determining the appropriate delivery of the tool for its future sustainability.
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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 | 0.015 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".