Immunomodulatory Medication Use in Newly Diagnosed Youth With Systemic Lupus Erythematosus
Bibliographic record
Abstract
OBJECTIVE: To examine glucocorticoid-sparing immunomodulatory medication use in youth with systemic lupus erythematosus (SLE) during their first year of care. METHODS: We conducted a retrospective cohort study using administrative claims for 2000 to 2013 from Clinformatics DataMart for youth ages 10-24 years with an incident diagnosis of SLE (≥3 International Classification of Diseases, Ninth Revision codes for SLE [710.0], each >30 days apart). We determined the proportion of subjects filling a prescription for immunomodulatory medications within 12 months of the first SLE code (index date). We used multivariable regression to examine associations between demographic/disease factors and time to prescription fill in the first year, and also between prescription fill at any time after the index date. RESULTS: We identified 532 youth with an incident SLE diagnosis, of which 413 (78%) had a glucocorticoid-sparing immunomodulatory prescription fill in the first year. Prescriptions for hydroxychloroquine and immunosuppressants were filled in the first year by 366 youth (69%) and by 182 (34%), respectively. Those with adult-onset (versus childhood-onset) disease were less likely to fill an immunomodulatory medication by 12 months. No other statistically significant associations were found, although there was increasing likelihood of immunomodulatory medication fills with each subsequent calendar year. CONCLUSION: Among youth with newly diagnosed SLE, hydroxychloroquine use is prevalent although not universal, and prescription immunosuppressant use is notably low during the first year of care. Further research is needed to identify factors contributing to suboptimal immunomodulatory medication use during the first year of care.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| 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".