Balloon‐assisted tracking: A must‐know technique to overcome difficult anatomy during transradial approach
Bibliographic record
Abstract
OBJECTIVE: To examine the use and outcomes of balloon-assisted tracking (BAT) technique for dealing with complexities of arm and chest vasculature during transradial approach (TRA) at a single high volume radial center. BACKGROUND: TRA has been used for coronary angiography and percutaneous coronary interventions (PCI) around the world. Different techniques have been described to address the anatomical issues and tortuosities for successful completion of coronary angiography and PCI. This study describes the use of BAT technique and associated outcomes during real world clinical practice. METHODS: Subjects comprised 63 patients, (out of total 8,245 patients between January 2011 and December 2012) in whom we encountered significantly complex anatomical course in radial, brachial, or subclavian region, leading to difficult advancement of a diagnostic or a guide catheter despite trying all standard maneuvers. In all of them BAT technique was used and they were retrospectively analyzed for the purpose of this study. RESULTS: About 63 (0.76%) of 8,245 patients met the study criteria. Twenty-five (39.7%) patients had very small RA. Twenty-two (34.9%) had severe RA tortuosity. Four (6.3%) had complex RA loops. Six (9.5%) had severe RA spasm and six (9.5%) had severe subclavian tortuosity and/or stenosis. We encountered technical failure in three (4.8%) patients (two had very small RA and one had 360 degree RA loop). CONCLUSION: BAT technique was useful to address the anatomical issues and tortuosities of radial, brachial, and subclavian vasculature during TRA.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".