Development of a Patient-centered Quality Measurement Framework for Measuring, Monitoring, and Optimizing Rheumatoid Arthritis Care in Canada
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to develop a patient-centered quality measurement framework to address a predefined vision statement and 7 strategic objectives for rheumatoid arthritis (RA) care that was developed in prior qualitative work with arthritis stakeholders. METHODS: One hundred forty-seven RA-related performance measures (PMs) were identified from a systematic review. A candidate list of 26 PMs meeting predefined criteria and addressing the strategic objectives previously defined was then assessed during a 3-round (R) modified Delphi. Seventeen panelists with expertise in RA, quality measurement, and/or lived experience with RA rated each PM on a 1-9 scale based on the items of importance, feasibility, and priority for inclusion in the framework during R1 and R3, with a moderated discussion in R2. PMs with median scores ≥ 7 on all 3 items without disagreement were included in the final set, which then underwent public comment. RESULTS: Twenty-one measures were included in the final framework (15 PMs from the Delphi and 6 published system-level measures on access to care and treatment). The measures included 4 addressing early access to care and timely diagnosis, 12 evidence-based care for RA and related comorbidities, 1 addressing patient participation as an informed partner in care, and 4 on patient outcomes. CONCLUSION: The proposed framework builds upon existing measures capturing early access to care and treatment in RA and adds important PMs to promote high-quality RA care and outcome measurement. In the next phase, the authors will test the framework in clinical practice in addition to addressing certain areas where no suitable PMs were identified.
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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 | 0.167 | 0.169 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".