Patterns of Medication Use in Systemic Lupus Erythematosus: A Multicenter Cohort Study
Bibliographic record
Abstract
OBJECTIVE: Evidence for the utility of medications in settings lacking randomized trial data can come from studies of treatment persistence. The present study was undertaken to examine patterns of medication use in systemic lupus erythematosus (SLE) using data from a large multicenter longitudinal cohort. METHODS: Prospectively collected data from the Asia Pacific Lupus Collaboration cohort including disease activity (SLE Disease Activity Index 2000 [SLEDAI-2K]) and medication details, captured at every visit from 2013-2018, were used. Medications were categorized as glucocorticoids (GCs), antimalarials (AM), and immunosuppressants (IS). Cox regression analyses were performed to determine the time-to-discontinuation of medications, stratified by SLE disease activity. RESULTS: Data from 19,804 visits of 2,860 patients were analyzed. Eight medication categories were observed: no treatment; GC, AM, or IS only; GC plus AM; GC plus IS; AM plus IS; and GC plus AM plus IS (triple therapy). Triple therapy was the most frequent pattern (31.4% of visits); single agents were used in 21% of visits, and biologics in only 3%. Time-to-discontinuation analysis indicated that medication persistence varied widely, with the highest treatment persistence for AM and lowest for IS. Patients with a time-adjusted mean SLEDAI-2K score of ≥10 had lower discontinuation of GCs and higher discontinuation of IS. CONCLUSION: Most patients received combination treatment. GC persistence was high, while IS persistence was low. Patients with high disease activity received more medication combinations but had reduced IS persistence, consistent with limited utility. These data confirm unmet need for improved SLE treatments.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".