Bibliographic record
Abstract
What Is the Issue? Canada’s Drug Agency (CDA-AMC) received a request to analyze CT exam volume trends over a 16-year period to inform policy decisions related to diagnostic imaging capacity and access. Because CT scans play a key role in diagnostics, growing demand may impact imaging services, wait times, and access. With the number of CT units growing from 419 in 2007 to 560 in 2022–2023, there is a need for analysis to better understand how this growth aligns with current demand and evolving clinical needs. Demand may be influenced by emerging clinical applications, such as biopsies, drainages, and other interventional uses of CT as well as changing population needs, available health care resources, and advancements in medical practices. Analyzing trends in overall and per capita exam volumes will provide insights to guide strategic decisions regarding resource allocation, workforce development, and potential expansion of diagnostic imaging capacity. What Did We Do? We examined trends in both total (absolute) and per capita (relative) CT exams using data from the Canadian Institute for Health Information (CIHI) and the Canadian Medical Imaging Inventory (CMII) — collected at 7 intervals between 2007 and 2022–2023. This also included reviewing the total number of CT units and CT units per capita. The analysis focused on 11 jurisdictions that maintained consistent CT capacity throughout the entire period. The results show how the use of CT machines has evolved nationwide. What Did We Find? Between 2007 and 2022–2023, there was growth in CT exam volumes across Canada, although this growth was not consistent every year: Total CT exam volumes increased from 3.38 million to 6.42 million, reflecting a national growth of 90%. Total CT exam volumes per thousand people increased from 102.5 to 160.2, representing a national increase in volume of 57%. CT exam growth varied across jurisdictions, with total exam increases ranging from 27% to 208%, and per capita volumes experiencing an increase of 10% to 120%. When comparing the growth in the jurisdictions to the national average between 2007 and 2022–2023, most jurisdictions saw their overall and per capita CT exam volumes rise above the national average. What Does This Mean? This report suggests that CT is being used more often, and the role of CT continues to evolve. Given the increasing use of CT, decision-makers may wish to consider capacity challenges and ensure that existing CT and supporting technologies, workflows, and processes are used with more efficiency. Addressing these areas may help reduce wait times, support recruitment and retention efforts, and ensure sufficient capacity to meet future demand. The variation in exam rates 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 alignment of 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".