A tool to assist rheumatologists to engage their lupus patients: the Purple Butterfly
Bibliographic record
Abstract
Objective: Translating the highly technical medical jargon of SLE into understandable concepts for patients, their families and individuals without expertise in SLE is a serious challenge. To facilitate communication and enable self-management in SLE, we aimed to create an innovative visual tool, the Purple Butterfly. Methods: We selected clinically representative criteria for SLE and transposed them as graphical features in an attractive and meaningful visual. We developed a script in R programming language that automatically transposes clinical data into this visualization. We asked SLE patients from a local cohort about the relevance, usefulness and acceptability of this visual tool in an online pilot survey. Results: The innovative Purple Butterfly features 11 key clinical criteria: age; sex; organ damage; disease activity; comorbidities; use of antimalarials, prednisone, immunosuppressants and biologics; and patient-reported physical and mental health-related quality of life. Each Purple Butterfly provides the health portrait of one SLE patient at one medical visit, and the automatic compilation of the butterflies can illustrate a patient's clinical journey over time. All survey participants agreed that they would like to use the Purple Butterfly to visualize the course of their SLE over time, and 9 of 10 agreed it should be used during their medical consultations. Conclusion: The Purple Butterfly nurtures effective doctor-patient communication by providing concise visual summaries of lupus patients' health conditions. We believe the Purple Butterfly has the potential to empower patients to take charge of their condition, enhance healthcare coordination and raise awareness about 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 | 0.002 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.033 | 0.006 |
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".