Long-Term Opioid Therapy Among Patients With Systemic Lupus Erythematosus in the Community: A Lupus Midwest Network (LUMEN) Study
Bibliographic record
Abstract
OBJECTIVE: There is little information about the epidemiology and factors associated with opioid therapy in systemic lupus erythematosus (SLE). We aimed to assess the prevalence of opioid therapy and explore factors associated with long-term opioid therapy (LTOT) in patients with SLE. METHODS: Patients with SLE were matched with controls without SLE in a population-based cohort on January 1, 2015. We captured demographics, manifestations of SLE, comorbidities (ie, fibromyalgia, mood disorders, osteoarthritis, chronic low back pain [CLBP], chronic kidney disease (CKD), avascular necrosis, osteoporosis, fragility fractures, and cancer), and the Area Deprivation Index (ADI). Opioid prescription data were used to assess the prevalence of LTOT, defined as contiguous prescriptions (gaps of < 30 days between prescriptions) and receiving opioid therapy for ≥ 90 days or ≥ 10 prescriptions before the index date. RESULTS: < 0.001), and 11% of patients with SLE were on LTOT vs 1% of controls without SLE. Among patients with SLE, acute pericarditis (odds ratio [OR] 3.92, 95% CI 1.78-8.66), fibromyalgia (OR 7.78, 95% CI 3.89-15.55), fragility fractures (OR 3.72, 95% CI 1.25-11.07), CLBP (OR 4.00, 95% CI 2.13-7.51), and mood disorders (OR 2.76, 95% CI 1.47-5.16) were associated with LTOT. We did not find an association between opioid therapy and ADI. CONCLUSION: Patients with SLE are more likely to receive LTOT than controls. Among patients with SLE, LTOT was associated with pericarditis and several comorbidities. However, LTOT was not associated with CKD despite the limited pain control options among these patients.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".