CHANGE IN GLUCOCORTICOID USE IN SYSTEMIC LUPUS ERYTHEMATOSUS IN A POPULATION-BASED INCEPTION COHORT OVER 4 DECADES: THE LUPUS MIDWEST NETWORK
Bibliographic record
Abstract
PV252 / #394 Poster Topic: AS24 - SLE-Treatment Background/Purpose We aimed to examine glucocorticoid (GC) use over 4 decades in a population-based incident cohort of patients with SLE. Methods Residents of Olmsted County, MN, with incident SLE meeting the 2019 EULAR/ACR SLE classification criteria between 1976-2018 were included. Index date was defined as the date of criteria fulfillment. GC use (oral daily prednisone equivalents) was abstracted from the index date until death or last follow-up through 12/31/2023. Cumulative daily dose of GCs in the first year after SLE incidence was calculated. Logistic regression models of any GC use in the first year and linear regression models of log cumulative GC dose in the first year adjusted for age and sex were used to examine time trends. Results The study included 198 patients with SLE (mean age 44.4 [SD 17.8] years; 166 [84%] female) who were subdivided into 4 decades (1976-1988, 1989-1998, 1999-2008 and 2009-2018) for analysis (Table). Patients in the most recent decade were somewhat older at diagnosis, and the population became more diverse over time. DMARD use in the first year after SLE incidence became more common over time, and the proportion of patients initiating GC increased over time (age and sex-adjusted p=0.045) while the proportion of patients using pulse GC in the first year remained stable over time. The starting dose of oral GCs decreased over the first 3 decades but increased again in the most recent decade (p=0.040). Cumulative GC use in the first year decreased over time (age and sex-adjusted p=0.005). This association persisted after additional adjustment for DMARD use. Most of the decline in cumulative GC use occurred in the 2000s with very little improvement subsequently. Table. Demographic and clinical characteristics of the incident cohort of patients with systemic lupus erythematosus in 1976-2018. Conclusions GC dosage at initiation had declined over time, but increased in the most recent decade, along with increases in the proportion of patients initiating GCs despite increased use of DMARDs. Cumulative GC use in the first year of SLE declined in the 2000s and has remained stable subsequently. These trends are mostly encouraging, but there is still room for more GC-sparing efforts in this population.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".