Nurse-led Care and Patients as Partners Are Essential Aspects of the Future of Rheumatology Care
Bibliographic record
Abstract
Living with chronic inflammatory arthritis (CIA), such as rheumatoid arthritis (RA) or spondyloarthritis (SpA), affects not only patients’ physical functioning but also emotional, psychological, and social aspects that have a global effect on their life situation as a whole1. The multidisciplinary team is important for the rheumatology care of patients with CIA, which should be delivered with an awareness of the patients’ whole life situation. The team should enable these patients to care for themselves and retain or regain optimum independence. The various professional categories in the team have distinct roles but collaborate to focus on the patients’ resources and needs2. Recommendations for rheumatology nursing management of CIA from the European League Against Rheumatism state that rheumatology nurses should participate in comprehensive disease management to control disease activity, as well as in identifying, assessing, and addressing psychosocial issues. This work is a valuable complement to the medical care and helps lower healthcare costs. For patients to achieve a greater sense of control, self-efficacy and empowerment, the nurse should meet the patient’s expressed needs and promote self-management skills3. Rheumatology research has completely changed the therapeutic arena over the past 2 decades, generating the development of the biological disease-modifying antirheumatic drugs (bDMARD) for a greater number of indications4. Despite this advance, research on nurse-led rheumatology care (NLC) has predominantly focused on patients with RA and conventional DMARD. A systematic review, including 4 randomized controlled trials (RCT) from 1994 to 2006, revealed in a metaanalysis (n = 431) that NLC compared to rheumatologist-led care (RLC) added value by improving patients’ perceived quality of life and knowledge, and lessening fatigue. While patient-reported outcomes such as functional status, satisfaction, pain, stiffness, and coping with arthritis favored NLC, there was insufficient evidence to draw conclusions5. Subsequent … Address correspondence to I. Larsson, School of Health and Welfare, Halmstad University Box 823, S-30118 Halmstad, Sweden. E-mail: Ingrid.larsson{at}hh.se
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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 | 0.018 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".