Reducing the Volume of Low-Value Outpatient MRI Joint Examinations in Patients ≥55 Years of Age
Bibliographic record
Abstract
Purpose: Magnetic resonance imaging (MRI) is not beneficial in patients with joint pain and concomitant osteoarthritis (OA). We attempt to determine whether evaluation of OA via X-rays can reduce inappropriate MRI and computed tomography (CT) arthrogram use. In our jurisdiction, CT arthrograms are used as surrogate tests because of MRI wait times. Materials and Methods: Our intervention required patients ≥55 years of age scheduled for outpatient MRI of the knee/hip/shoulder at an urban hospital to have X-rays (weight bearing when appropriate) from within 1 year. Red flags (ie, neoplasm, infection) were identified for which MRI would be indicated regardless. Through review of radiographs on picture archiving and communication system/digital media and use of the validated Kellgren-Lawrence (KL) OA scale, radiologists assessed the presence and degree of OA. A finding of significant OA (KL > 2) without red flags would preclude MRI. Monthly averages of MRI and CT arthrogram examinations were measured 33 months before and 23 months following introduction of the intervention. Results: The proportion of protocoled MRI requisitions that were avoided was 21%. If extrapolated to the province of British Columbia, 2419 of 11 700 examinations could have been prevented in the past year. The average monthly number of knee/hip/shoulder MRI examinations as a percentage of total MRI examinations decreased from 4.9% to 4.3% ( P < .02) following the intervention. The average monthly number of knee/hip/shoulder CT arthrogram examinations decreased from 20.6 to 12.1 ( P < .0001). Conclusion: We were able to decrease the number of MRI and CT arthrogram examinations in patients ≥55 years of age with joint pain by implementing an evaluation for OA via recent X-ray imaging.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".