Improving Outcomes in Systemic Lupus Erythematosus: The Importance of Access to Medications
Bibliographic record
Abstract
Systemic lupus erythematosus (SLE) is a heterogeneous autoimmune disease with the potential to affect multiple organ systems and to lead to significant morbidity and mortality. SLE disproportionately affects women and racial or ethnic minority populations, and health disparities have been described for SLE incidence, prevalence, outcomes, and mortality.1 The most significant contributor to mortality overall in SLE is the presence of renal disease, and renal disease is more common in racial and ethnic minorities.1 Renal disease also necessitates more aggressive management with immunosuppressant medications. Health disparities are often multifactorial. In SLE, these have been most commonly attributed to socioeconomic status, environmental exposures, access to high-quality health care, racism, and genetic factors.1 Medication adherence can improve outcomes, but there have been limited studies evaluating the extent to which cost concerns factor into medication adherence and the extent to which medication cost concerns affect outcomes in SLE. In this issue of The Journal of Rheumatology , Aguirre and colleagues assessed medication cost concerns and their relationship to patient-reported outcomes (PROs) in a multiethnic cohort of patients with SLE.2 Specifically, the California Lupus Epidemiology Study (CLUES) is a population-based cohort study of people with physician-confirmed diagnosis of SLE recruited since 2014. Participants in CLUES completed a survey about medication cost concerns and PROs were also collected. Of note, medication cost concerns included a broad set of patient-reported concerns (including having difficulty affording SLE medications, skipping doses, delaying refills, requesting lower-cost alternatives, purchasing medications outside the country, or applying for patient assistance programs). The PROs measured and analyzed included the Systemic Lupus Erythematosus Activity Questionnaire (SLAQ), Patient Health Questionnaire depression scale (PHQ-8), several representative physical health and social health domains of the Patient-Reported Outcomes Measurement Information System (PROMIS) measures, and the Brief Index of Lupus Damage (BILD). Data were analyzed … Address correspondence to Dr. E.D. Ferucci, 3900 Ambassador Drive, Suite 201, Anchorage, AK 99508, USA. Email: edferucci{at}anthc.org.
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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 | 0.005 | 0.042 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".