P.118 Radial to femoral “through and through” access for high grade ostial subclavian and innominate artery stenoses: a novel technique
Bibliographic record
Abstract
Background: Endovascular approaches are typically preferred to open surgical techniques for symptomatic subclavian/innominate artery stenosis. Due to individual patient anatomy, endovascular treatment from a conventional femoral arterial approach can be technically challenging. Our alternative technique using a combined radial to femoral artery approach can facilitate an otherwise challenging revascularization procedure. Methods: Retrospective analysis between November 2017 to March 2021 yielded five procedures (in four patients) using a combined radial to femoral “through and through” access and stenting technique. Results: All patients presented with hypoperfusion symptoms, either to their extremities, brain, or both. Technical success was achieved in 100% of the five vessels treated in four patients with symptomatic subclavian/innominate artery stenosis using this approach. One of the patients developed a recurrent stenosis after 40 months, requiring a repeat procedure. Three patients received treatment to the left subclavian artery and one to the innominate artery. All of the patients experienced marked symptomatic improvement without significant complications. Conclusions: A combined radial to femoral “through and through” access technique is a simple and safe method to achieve successful recanalization of high grade symptomatic ostial stenoses of the subclavian and innominate arteries.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".