Optimizing Musculoskeletal Imaging Referrals: Making Wise Choices a Knee-Jerk Reaction
Bibliographic record
Abstract
Purpose: To develop Choosing Wisely Canada (CWC) recommendations for musculoskeletal (MSK) imaging indications, informed by the 2024 Canadian Association of Radiologists (CAR) Musculoskeletal System Diagnostic Imaging Referral Guideline. Methods: A Steering Committee comprising multidisciplinary MSK experts was convened to guide recommendation development. Using a two-round Delphi method, committee members selected the top 3 scenarios from the CAR MSK referral guidelines deemed most impactful for addressing overuse. Recommendations based on these scenarios were then drafted using the CWC format. Results: The 3 recommendations developed are: (1) Don’t order MRI without first considering ultrasound for the assessment of rotator cuff pathology and bursitis; (2) Don’t order MRI of the hip or knee when x-ray demonstrates greater than mild osteoarthritis, unless recommended by a musculoskeletal specialist; and (3) Don’t order MRI of the hip just based on x-ray features of femoroacetabular impingement unless there are clinical signs and symptoms of joint impingement. Conclusions: This project represents a knowledge translation initiative to disseminate updated MSK imaging guideline recommendations. It strengthens the collaboration between CAR and CWC and establishes a reproducible structured consensus approach that can be applied to developing additional CWC imaging recommendations across the remaining 12 CAR referral guidelines in other subspecialties. This work supports value-based radiology, promoting optimized resource use.
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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 | 0.136 | 0.276 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.010 | 0.013 |
| 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".