Barriers to Healthcare in a Multiethnic Cohort of Systemic Lupus Erythematosus (SLE) Patients: Patient and Physician Perceptions
Bibliographic record
Abstract
Objective Barriers to medical care may influence health status. It is unclear whether problems with access can predict clinical outcomes in lupus. This study aimed to determine whether care barriers are associated with increased disease activity and damage in a multi-center, multiethnic SLE cohort. We also compared concordance between care barriers as reported by the patient and lupus specialist. Methods Data from SLE patients in 12 Canadian centers collected at annual visits, including demographics, treatment, disease activity and damage were analyzed. Results 654 patients were enrolled with ethnic groups being Caucasian [CC] (64%), Aboriginal [ABO] (9%), Asian [AS] (21%), and Black [BLK] (6%). 50.8% had at least one barrier to care including travel to a rheumatologist (32.0%), waiting to see a rheumatologist and cost of medications. Access to medication and costs were significantly associated with co-morbidity (p < 0.001, p = 0.04). There were significant associations between ethnicity and any physician perceived care barrier < p < 0.001), mostly in Aboriginal. Doctors identified half of patients who had access to medication problems (p = 0.003) and the relationship between doctors and patients identifying similar care barriers was weak (r = 0.09). A lower total household income significantly predicted the presence of any care barrier (p < 0.001). Conclusions Despite access to a lupus specialist many care barriers were identified, although we found few associations between care barriers and patient outcomes. The cost of medication was related to SLE disease activity; however, we cannot determine if this was cause or effect. Care barriers identified by lupus patients are significantly underestimated by physicians.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".