Transradial Access for Interventional Radiology: Single-Centre Procedural and Clinical Outcome Analysis
Bibliographic record
Abstract
PURPOSE: The study sought to describe a single centre's technical approach to transradial intervention and report on clinical outcomes and safety. METHODS: A total of 749 transradial access (TRA) procedures were performed at a single hospital in 562 patients (174 women and 388 men). Procedures included 445 bland embolizations or chemoembolizations of the liver, 88 uterine artery embolizations, and 148 procedures for Selective Internal Radiation Therapy (Y90), which included mapping and administration. The mean age of the patients was 62 years (range 27-96 years). RESULTS: Four cases (0.5%) required crossover to transfemoral (tortuous anatomy, inability to secure a stable position for embolization, vessel spasm and base catheter not being of a sufficient length). A single asymptomatic, short-segment radial artery occlusion occurred (0.3%), 3 patients (0.4%) developed small hematomas postprocedurally, and 2 patients (0.7%) had transient neurological pain, which was resolved within a week without treatment. It was found that 98% of patients who had a previous femoral access procedure would choose radial access for subsequent procedures. CONCLUSIONS: Transradial access is a safe, effective technique, with a learning curve; however, this procedure has the potential to significantly improve departmental workflow and cost savings for the department and patient experience.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".