Canadian Association of Radiologists Diagnostic Imaging Referral Guidelines: a guideline development protocol
Bibliographic record
Abstract
BACKGROUND: Comprehensive diagnostic imaging referral guidelines are an important tool to assist referring clinicians and radiologists in determining the safest and best-clinical-value diagnostic imaging study for their patients; the Canadian Association of Radiologists (CAR) last produced its diagnostic imaging referral guidelines in 2012. In partnership with several national organizations, referring clinicians, radiologists, and patient and family advisors from across Canada, the association is redoing its referral guidelines using a new methodology for guideline development, and these guideline recommendations will be suited for integration into clinical decision support systems. METHODS: Expert panels of radiologists, referring clinicians and a patient advisor will work with epidemiologists at the CAR to create guidelines across 13 clinical sections. The expert panel for each section will first create a comprehensive list of clinical and diagnostic scenarios to include in the guidelines. Canadian Association of Radiologists epidemiologists will then conduct a systematic rapid scoping review to identify systematically produced guidelines from other guideline groups. The corresponding expert panel will develop diagnostic imaging recommendations for each clinical and diagnostic scenario using the recommendations identified from the scoping review and contextualize them to the Canadian health care systems. The expert panels will accomplish this using an adapted Grading of Recommendations Assessment, Development and Evaluation framework, which reflects the benefits and harms, values and preferences, equity, accessibility, resources and cost. INTERPRETATION: Freely available, up-to-date, comprehensive Canadian-specific diagnostic imaging referral guidelines are needed. A transparent and structured guideline-development approach will aid the CAR and its partners in producing guidelines across its 13 sections.
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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 | 0.065 | 0.158 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.014 | 0.019 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.009 | 0.008 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.035 | 0.012 |
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".