Impact of residential area on the management of rheumatoid arthritis patients initiating their first biologic DMARD
Bibliographic record
Abstract
Access to care and management of Rheumatoid Arthritis (RA) patients may differ based on residential area. We described differences in the profile of patients initiating their first biologic disease modifying antirheumatic drug (bDMARD) based on their residential area type.Cross-sectional analysis of 793 adult RA patients in the longitudinal Ontario Best Practices Research Initiative (OBRI) registry initiating their first bDMARD <30 days prior to or anytime post-enrolment. Patient residential and clinic areas (rural vs. urban) were classified using 2 methods: postal codes and Statistics Canada population centres. Sociodemographics, disease characteristics, and RA medications (tumor necrosis factor inhibitor [TNFi] vs. non-TNFi, concurrent use of conventional synthetic DMARDs [csDMARDs], and intravenous [IV] vs. subcutaneous [SC] bDMARD) at initiation of first bDMARD were contrasted between residential area types.Other than marital status, first language, and race (higher proportion of married, English speaking, Caucasian patients in rural areas), no significant differences were observed in the demographic and disease characteristics of patients living in rural and urban areas. In multivariate analysis, there was no association between residential area type and type of bDMARD use, concurrent csDMARD(s) use or route of bDMARD. However, patients living farther from their treating clinic were significantly less likely to initiate IV bDMARD. Female rheumatologist and rural clinic location were independently associated with lower odds of IV bDMARD use.The use of SC vs. IV bDMARD was associated with being seen in a clinic located in a rural area, being treated by a female rheumatologist, and living farther from treating clinic. These results suggest possible prescription bias in bDMARD selection and/or patient preferences due to convenience.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".