Bibliographic record
Abstract
Transradial access (TRA) is a key strategy to avoid bleeding during percutaneous coronary intervention (PCI) and coronary angiography, as it reduces the risk of bleeding associated with transfemoral access (TFA). The distal radial artery (dRA) is located at the anatomical snuffbox, or radial fossa, which is a triangular depression on the lateral aspect of the dorsum of the hand. Distal transradial access (dTRA) at the anatomical snuffbox has recently gained interest as a non-femoral alternative access route for vascular procedures. Opportunities and challenges in the TRA/dTRA space include adoption of different and more complex procedures, technical innovation in access and haemostasis, and financial savings for institutions and healthcare systems. Historically, the adoption of TRA has been concentrated among interventional cardiologists, with much less adoption in the interventional radiology, vascular surgery, and neurointerventional radiology communities. This article highlights key aspects of the Think Radial webinar, an educational training session conducted by Merit Medical. The training session was conducted by three experts: Sandeep Nathan, Department of Cardiology, University of Chicago Medicine, Illinois, USA; Darren Klass, Department of Radiology, University of British Columbia, Vancouver, and Department of Radiology, Vancouver General Hospital, British Columbia, Canada; and Ajit Puri, New England Center for Stroke Research, Department of Radiology, University of Massachusetts Medical Center, Worcester, USA, all of whom have a wealth of experience in vascular access. The experts provided valuable insights into topics such as why and how to adopt a dTRA approach, the value of ultrasound for guided vascular access, and dTRA and ultrasound tips and techniques. The experts also explored the challenges in transradial intervention and management of radial access complications. Drawing on their individual specialist knowledge, the experts concluded with presentations on PCI, radial approaches in interventional radiology, and dRA access in neurointervention. The aim of this educational article is to disseminate key information on TRA provided by the experts to guide and update healthcare practitioners, particularly interventional cardiologists and interventional radiologists, working in vascular access.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.022 | 0.006 |
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".