Interventional radiology training in the UK: a view from within—a national survey
Bibliographic record
Abstract
OBJECTIVE: Interventional radiology (IR) training in the UK has evolved since recognition as a subspecialty in 2010 and introduction of a new curriculum in 2021. The changing landscape, increasing workload and COVID-19 have affected training. The purpose of this study was to review trainees' perspectives on training and develop strategies to further improve training. METHODS: Online survey approved by the British Society of Interventional Radiology Council distributed to British Society of Interventional Radiology Trainee members between 9 March 22 and 25 March 2022. The survey was open to all UK based ST4-6 IR trainees and fellows. Descriptive and thematic analysis was undertaken. RESULTS: 43 responses were received from 17/19 UK training regions. Females represented 10% (4/41) and 5% (2/43) less than full time (LTFT) trainees. 82% (31/38) felt their curriculum was suitable for their training and 28/38 (74%) were satisfied with IR training. Vascular IR, Interventional Oncology, paediatrics and stroke thrombectomy were identified as areas of training desiring improvement. 45% (18/40) stated exposure to IR led clinics and 17.5% (7/40) to IR led ward rounds. Only 6/38 (15.7%) received structured IR teaching at least once a month. Approximately, a third of respondents (13/38) stated training opportunities were significantly compromised secondary to COVID-19. CONCLUSION: This survey shows overall good satisfaction with IR training. However, improved training opportunities in vascular IR, interventional oncology, paediatric IR and stroke thrombectomy are required. In addition, access to clinics, ward rounds and protected time for research is needed to improve training quality. ADVANCES IN KNOWLEDGE: New national UK IR training survey.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".