A Day in the Life of MRI: The Variety and Appropriateness of Exams Being Performed in Canada
Bibliographic record
Abstract
PURPOSE: This study aimed to determine the volumes and types of magnetic resonance imaging exams being performed across Canada, common indications for the exams, and exam appropriateness using multiple evaluation tools. METHODS: Thirteen academic medical institutions across Canada participated. Data were obtained relating to a single common day, October 1, 2014. Patient demographics, type by anatomic region and indication for imaging were analysed. Each exam was assessed for appropriateness via the Canadian Association of Radiologists Referral Guidelines and the American College of Radiology Appropriateness Criteria. The Alberta and Saskatchewan spine screening forms and the Alberta knee screening form were also used where applicable. The proportion of exams that were unscorable, appropriate, and inappropriate was determined. Exam-level results were compared between the 2 main evaluation tools. RESULTS: Data were obtained for 1087 relevant exams. There were 591 women and 460 men. 36 requisitions did not indicate the patient's sex. Brain exams were the most common, comprising 32.5% of the sample. Cancer was the most common indication. Overall, 87.0%-87.4% of the MR exams performed were appropriate; 6.6%-12.6% were inappropriate, based on the 2 main evaluation tools. Results differed by anatomic region; spine exams had the highest proportion, with nearly one-third of exams deemed inappropriate. CONCLUSION: Variations by anatomic region indicate that focused exam request evaluation or screening methods could substantially reduce inappropriate 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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".