Nurse-Led Care for Stable Patients with Rheumatoid Arthritis: Quality of Care in Routine Practice Compared to the Traditional Rheumatologist-Led Model
Bibliographic record
Abstract
INTRODUCTION: This pragmatic non-inferiority study assessed quality of care within a nurse-led care (NLC) model for stable patients with rheumatoid arthritis (RA) compared to the traditional rheumatologist-led care (RLC) model. METHODS: Data were collected through a chart review. Baseline demographic and clinical characteristics were compared using Chi-square test and t test. The primary outcome measure was the percentage of patients being in remission or low disease activity (R/LDA) with the Disease Activity Score (DAS-28) ≤ 3.2 at 1-year follow-up. Process measures included the percentages of patients with chart documentation of (1) comorbidity screening; (2) education on flare management, and (3) vaccinations screening. Outcomes were summarized using descriptive statistics. RESULTS: Each group included 124 patients. At baseline, demographic and clinical characteristics were comparable between the groups for most variables. Exceptions were the median (Q1, Q3) Health Assessment Questionnaire Disability Index scores [0 (0, 0.25) in NLC and 0.38 (0, 0.88) in RLC, p = 0.01], and treatment patterns with 3% of NLC and 38% of RLC patients receiving a biologic agent, p = 0.01. NLC was non-inferior to RLC with 97% of NLC and 92% of RLC patients being in R/LDA at 1-year follow-up. Patients in the NLC group had better documentation across all process measures. CONCLUSIONS: This study provided real-world evidence that the evaluated NLC model providing protocolized follow-up care for stable patients with RA is effective to address patients' needs for ongoing disease monitoring, chronic disease management, education, and support.
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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 | 0.017 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".