Development of System-level Performance Measures for Evaluation of Models of Care for Inflammatory Arthritis in Canada
Bibliographic record
Abstract
OBJECTIVE: To develop system-level performance measures for evaluating the care of patients with inflammatory arthritis (IA), including rheumatoid arthritis (RA), psoriatic arthritis, ankylosing spondylitis, and juvenile idiopathic arthritis. METHODS: This study involved several methodological phases. Over multiple rounds, various participants were asked to help define a set of candidate measurement themes. A systematic search was conducted of existing guidelines and measures. A set of 6 performance measures was defined and presented to 50 people, including patients with IA, rheumatologists, allied health professionals, and researchers using a 3-round, online, modified Delphi process. Participants rated the validity, feasibility, relevance, and likelihood of use of the measures. Measures with median ratings ≥ 7 for validity and relevance were included in the final set. RESULTS: Six performance measures were developed evaluating the following aspects of care, with each measure being applied separately for each type of IA except where specified: waiting times for rheumatology consultation for patients with new onset IA, percentage of patients with IA seen by a rheumatologist, percentage of patients with IA seen in yearly followup by a rheumatologist, percentage of patients with RA treated with a disease-modifying antirheumatic drug (DMARD), time to DMARD therapy in RA, and number of rheumatologists per capita. CONCLUSION: The first set of system-level performance measures for IA care in Canada has been developed with broad input. The measures focus on timely access to care and initiation of appropriate treatment for patients with IA, and are likely to be of interest to other arthritis care systems internationally.
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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 | 0.088 | 0.183 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.011 | 0.016 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 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".