P.131 Mapping the neurointerventional radiology landscape in Canada: trends in growth, accessibility, and training opportunities
Bibliographic record
Abstract
Background: Neurointerventional radiology (NIR) is a growing field, offering minimally invasive treatments for cerebrovascular conditions like ischemic stroke. However, no comprehensive analysis of the current NIR landscape in Canada exists. This study aims to evaluate the NIR landscape through analysis of hospital-based services and training programs. Methods: Publicly available hospital data, fellowship programs, and national workforce statistics were analyzed to assess the expansion of NIR centers, practitioners, and services in Canada. The analysis focused on temporal trends in geographic distributions, specialists, and training programs. Results: From 2022 to 2024, the number of NIR centers increased by 20% (from 25 to 30), with new sites established in British Columbia, Quebec, and Newfoundland. Seven accredited RCPSC NIR training programs were identified, with 2 new programs expected to begin training fellows by 2030. Annual trainee enrollment also increased by about 10% per year, with over 50% being from radiology backgrounds. Endovascular thrombectomy, the most common NIR procedure, has seen an annual volume increase of approximately 13% since 2019. Conclusions: NIR is experiencing substantial growth in Canada across centers and training sites, aligning with public health goals. However, continued investment in infrastructure and workforce development is required to ensure equitable access to life-saving neurointerventional therapies nationally.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".