Development of a Canadian Core Clinical Dataset to Support High-quality Care for Canadian Patients with Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: To develop a Canadian Rheumatoid Arthritis Core Clinical Dataset (CAN-RACCD) to standardize documentation encouraging high-quality care. METHODS: A set of candidate elements was drafted through meetings with 27 rheumatologists, researchers, and patients, and supplemented with focused literature reviews. A 3-round online-modified Delphi consensus process was held with rheumatologists (n = 26), allied health professionals (n = 7), and patients (n = 4); for the remainder there was no demographic information. Participants rated both the importance and feasibility of documenting candidate elements on a Likert scale of 1-9, contributed to an online moderated discussion, and re-rated the elements for inclusion in the CAN-RACCD. Elements were included in the final set if importance and feasibility ratings had a median score of ≥ 6.5 and there was no disagreement among participants. RESULTS: Fifty-five individual elements in 10 subgroups were proposed to the Delphi participants: measures of RA disease activity; dates to calculate waiting times, disease duration, and disease-modifying antirheumatic drug start; comorbidities; smoking status; patient-reported pain and fatigue; physical function; laboratory and radiographic investigations; medications; clinical characteristics; and vaccines. All groups were included in the final set, with the exception of vaccination status. Additionally, 3 individual elements from the smoking subgroup were eliminated with a recommendation to record smoking status as never/ever/current, and 2 elements relating to coping and effect of fatigue were eliminated due to low feasibility and importance ratings. CONCLUSION: The CAN-RACCD stands as a national recommendation on which data elements should be routinely collected in clinical practice to monitor and support high-quality RA care.
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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 | 0.059 | 0.133 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".