Bibliographic record
Abstract
What Is the Issue? Canada’s Drug Agency (CDA-AMC) received a request related to the average annual number of CT and MRI examinations performed per site and per unit across the country. CT and MRI use in Canada has nearly doubled since 2007, yet there is limited recent publicly available information at the national and jurisdictional levels that reports examination volumes by geographic setting (urban, rural, and remote) or facility type (academic versus community). Detailed data can support informed resource planning, efficient workload management, and equitable service delivery — and CDA-AMC is uniquely positioned to fill this gap by collecting and analyzing national imaging data across geographic settings and facility types. What Did We Do? CDA-AMC leveraged data from the 2022–2023 Canadian Medical Imaging Inventory National Survey, including site-level and unit-level examination volumes. In total, 178 of 394 CT sites and 115 of 239 MRI sites provided sufficient data to estimate average annual examination volumes per site and per unit. What Did We Find? Nationally, CT sites operated an average of 1.40 units and performed 16,350 annual examinations per site (12,900 examinations performed per unit), while MRI sites averaged 1.49 units with 9,033 annual examinations performed per site (5,850 examinations performed per unit). Urban facilities had higher capacity and annual examination volumes, with urban CT sites averaging 1.69 units and 23,036 examinations performed per site, and urban MRI sites averaging 1.64 units and 10,501 annual examinations performed per site. Rural and remote sites had fewer units and lower examination volumes: rural CT sites averaged 1.04 units with 8,303 examinations performed; remote CT sites averaged 1 unit with 5,999 examinations performed; rural MRI sites averaged 1 unit with 4,522 examinations performed; and remote MRI sites averaged 1 unit with 3,198 examinations performed. Academic sites operated more units and performed significantly more examinations than community sites. Academic CT sites averaged 2.14 units and 29,024 annual examinations performed per site, compared to 1.21 units and 13,024 annual examinations performed at community sites. Academic MRI sites averaged 2 units and 12,500 annual examinations performed, versus 1.23 units and 7,323 annual examinations performed for community sites. What Does This Mean? Understanding the differences across urban, rural, remote, academic, and community settings can inform appropriate distribution of imaging resources. These findings may help decision-makers understand site-level workloads and where additional scanners or staffing would be most beneficial. Highlighting sites with higher annual scan volumes can guide efforts to support technologist capacity through targeted recruitment, training, or optimized scheduling.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.020 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".