Understanding the Canadian Interventional Radiology Landscape Through the Canadian Association of Interventional Radiology Database: A Descriptive Study
Bibliographic record
Abstract
Background: Physicians in Canada are disproportionately concentrated in urban centers, with rural regions facing significant gaps in specialist care. The geo-spatial characteristics of Interventional Radiology (IR) in Canada have not been systematically studied. This study examines the distribution of Canadian Association of Interventional Radiology (CAIR) members and the sociodemographic characteristics of the populations they serve. Methods: A list of active members of the Canadian Association of IR (CAIR) was obtained in May 2025. Data from the Canadian Census was used to gather census division (CD)-level information on population size and demographics including data on ethnicity, income, education, employment, and income. CDs were grouped and compared between those with access to IR care and those without, compared between top and bottom quartiles for each population characteristic, and mapped. Results: Of 293 CDs in Canada, only 48 (19.6%) had at least one CAIR IR, leaving 31.6% of the national population without local access. CAIR IRs were strongly clustered in high-population CDs, with Toronto, Vancouver, and Montreal alone accounting for 56.5% of IRs. CDs with CAIR IRs had significantly higher mean populations (527 322 vs 47 675; P < .001), median incomes ($82 760 vs $75 821; P = .002), and educational attainment (31% vs 16%; P < .001). Conversely, CDs with higher proportions of the population being Indigenous were less likely to have CAIR IRs (5% vs 14%; P < .001). Conclusion: CAIR member IRs are disproportionately concentrated in large urban centers, leaving substantial geographic disparities. Addressing this maldistribution will require coordinated strategies, including outreach models, telehealth integration, and national workforce planning.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.024 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".