Overall Impact of the COVID-19 Pandemic on Interventional Radiology Services: A Canadian Perspective
Bibliographic record
Abstract
PURPOSE: The aim of this national survey was to assess the overall impact of the coronavirus disease 2019 (COVID-19) pandemic on the provision of interventional radiology (IR) services in Canada. METHODS: An anonymous electronic survey was distributed via national and regional radiology societies, exploring (1) center information and staffing, (2) acute and on-call IR services, (3) elective IR services, (4) IR clinics, (5) multidisciplinary rounds, (6) IR training, (7) personal protection equipment (PPE), and departmental logistics. RESULTS: Individual responses were received from 142 interventional radiologists across Canada (estimated 70% response rate). Nearly half of the participants (49.3%) reported an overall decrease in demand for acute IR services; on-call services were maintained at centers that routinely provide these services (99%). The majority of respondents (73.2%) were performing inpatient IR procedures at the bedside where possible. Most participants (88%) reported an overall decrease in elective IR services. Interventional radiology clinics and multidisciplinary rounds were predominately transitioned to virtual platforms. The vast majority of participants (93.7%) reported their center had disseminated an IR specific PPE policy; 73% reported a decrease in case volume for trainees by at least 25% and a proportion of trainees will either have a delay in starting their careers as IR attendings (24%) or fellowship training (35%). CONCLUSION: The COVID-19 pandemic has had a profound impact on IR services in Canada, particularly for elective cases. Many centers have utilized virtual platforms to provide multidisciplinary meetings, IR clinics, and training. Guidelines should be followed to ensure patient and staff safety while resuming IR services.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".