Bibliographic record
Abstract
What Is the Issue? Canada’s Drug Agency (CDA-AMC) received a request to analyze MRI exam trends over 16 years to inform policy decisions related to diagnostic imaging capacity and access. Because MRI scans play a key role in diagnostics, growing demand may impact imaging services, wait times, and access. With the number of MRI units growing from 222 in 2007 to 432 in 2022–2023, analysis is needed to better understand how growth aligns with demand. Demand may be influenced by emerging clinical applications of MRI — such as MRI-guided biopsies and surgeries, and other detailed cardiac assessments — along with changing population needs, health care resources, and medical advancements. Analyzing overall and per capita exam trends will provide insights to guide strategic decisions regarding resource allocation, workforce development, and potential diagnostic imaging capacity expansion. What Did We Do? We examined trends in both total (absolute) and per capita (relative) MRI exams using data from the Canadian Institute for Health Information and the Canadian Medical Imaging Inventory — collected at 7 intervals between 2007 and 2022–2023. We also reviewed MRI unit counts and units per capita. The analysis focused on 10 jurisdictions with consistent MRI capacity throughout the entire period, showing how MRI use evolved nationwide. What Did We Find? Between 2007 and 2022–2023, there was growth in MRI exam volumes across Canada, although this growth was not consistent annually: Total MRI exam volumes increased from 1.02 million to 2.21 million, reflecting a national growth of 117%. Total MRI exam volumes per 1,000 people increased from 30.9 to 55.6, representing a national growth rate of 80%. MRI exam growth varied across jurisdictions, with total exam increases ranging from 42% to 239% and per capita volumes experiencing an increase ranging from 24% to 170%. No jurisdiction experienced a decrease in either total or per capita MRI exam volume. When comparing growth to the national average, half the jurisdictions were below average in both total and per capita exams, while the other half exceeded the national average in overall or per capita exam volume, or both. What Does This Mean? This suggests that MRI is being used more, and the role of MRI continues to evolve. Given the increasing use, decision-makers may wish to consider capacity challenges, ensuring efficient use of existing MRI and supporting technologies, workflows, and processes. Addressing these areas may help reduce wait times, support recruitment and retention efforts, and ensure sufficient capacity to meet future demand. The variation across jurisdictions highlights the importance of tailoring solutions to local needs and contexts. The insights gained from this report can inform strategic decisions related to resource allocation, workforce development, and aligning diagnostic imaging capacity with evolving health care needs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".