REAL-WORLD ASSESSMENT OF PATTERNS OF PAIN TREATMENT IN SYSTEMIC LUPUS ERYTHEMATOSUS: A PATHWAY VISUALIZATION STUDY USING EHR
Bibliographic record
Abstract
PV254 / #673 Poster Topic: AS24 - SLE-Treatment Background/Purpose To characterize trends and demographic differences in pain management modalities among patients with SLE and assess the evolution of treatment strategies over the past 2 decades. Methods We retrospectively analyzed electronic health records from 2005-2024 at a single academic center. Using sequence analysis and Sankey diagrams, we mapped the progression of pain management modalities, including corticosteroids, opioids, NSAIDs, and non-pharmaceutical therapies, within 2 timeframes: 2005-2014 and 2015-2024. We evaluated transitions between therapies, demographic influences (age, sex, race/ethnicity), and shifts in treatment patterns over time. Results A total of 381 patients received at least 1 pain management modality during 2005-2014, while 387 patients received such modalities from 2015-2024 (Table 1, Figure 1). Steroid and opioid use as initial treatments decreased significantly from 59% to 51% and 28% to 23%, respectively, between the 2 periods. NSAIDs emerged as a primary choice, increasing from 19% to 29%. Additionally, younger patients and males in recent years received more diverse prescriptions, with observed racial differences indicating that Black and Hispanic patients were more likely to receive multiple pain management modalities. The overall number of pain prescriptions also declined, reflecting an adaptive, multimodal approach to pain management in SLE (Figure 2). Table 1. Figure 1. Figure 2. Conclusions Our findings reveal an ongoing shift toward diversified, multimodal pain management in SLE with reduced reliance on steroids and opioids, particularly among younger patients. Demographic variations underscore the need for personalized and equitable pain management strategies in SLE, supporting future guidelines that incorporate both safety and patient-specific factors.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".