Development of an interdisciplinary early rheumatoid arthritis care pathway
Bibliographic record
Abstract
BACKGROUND: To develop an interdisciplinary care pathway for early rheumatoid arthritis (RA) including referral triage, diagnosis, and management. METHODS: Our process was a four-phase approach. In Phase 1, an anonymous survey was electronically distributed to division rheumatologists. This provided data to a small interprofessional working group of rheumatology team members who drafted an initial care pathway informed by evidence-based practice in Phase 2. In Phase 3, an education day was held with approximately 40 physicians (rheumatologists and rheumatology residents), members of our interprofessional team, and two clinic managers to review the proposed care elements through presentations and small group discussions. The care pathway was revised for content and implementation considerations based on feedback received. Implementation of the care pathway and development of strategies for evaluation is ongoing across multiple practice sites (Phase 4). RESULTS: Our care pathway promotes an approach to patient-centered early RA care using an interdisciplinary approach. Care pathway elements include triage processes, critical diagnostics, pre-treatment screening and vaccinations, and uptake of suggested RA pharmacologic treatment using shared decision-making strategies. Pathway implementation has been facilitated by nursing protocols and evaluation includes continuous monitoring of key indicators. CONCLUSION: The 'Calgary Early RA Care Pathway' emphasizes a patient-centered and interdisciplinary approach to early RA identification and treatment. Implementation and evaluation of this care pathway is ongoing to support, highest quality care for patients.
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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 | 0.025 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".