Choosing Wisely: The Canadian Rheumatology Association Pediatric Committee’s List of Items Physicians and Patients Should Question
Bibliographic record
Abstract
OBJECTIVE: To develop a list of tests or treatments frequently used in pediatric rheumatology practice that may be unnecessary based on existing evidence. METHODS: A Choosing Wisely (CW) working group composed of 16 pediatric rheumatologists, 1 allied health professional, 1 parent, and 1 patient used the Delphi method to generate, rank, and refine a list of tests and treatments that may be unnecessary or harmful. The items with the highest content agreement and perceived impact were presented in a survey to all Canadian Rheumatology Association (CRA) physicians who practice pediatric rheumatology. Respondents were asked to rate their agreement and impact, and to rank the items. Five items with the highest composite scores and 2 additional items selected by the CW working group were put forward for literature review. RESULTS: The initial Delphi procedure generated 80 items. After 3 rounds, the list was narrowed to 13 items. The survey was completed by 41/81 (51%) CRA pediatric members across Canada. Respondent characteristics were similar to those of the CRA pediatric membership for self-reported gender, geographical location, and career stage. The highest composite score items were antinuclear antibody testing, drug toxicity monitoring, HLA-B27 testing, rheumatoid factor/anticyclic citrullinated peptide testing, and Lyme serology testing. Two additional items (numerous or repeated intraarticular corticosteroid injections, and autoinflammatory diseases genetic testing) were also selected. Literature review was performed for these 7 highest priority items. CONCLUSION: We have identified areas for quality improvement in the evaluation and treatment of rheumatic diseases in Canadian children.
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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 | 0.021 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".