Wire countertraction for sheath placement through stenotic and tortuous veins: The “body flossing” technique
Bibliographic record
Abstract
BACKGROUND: Innominate vein stenosis and venous tortuosity are common findings during cardiac implantable electronic device upgrades or replacements and present a challenge to the implanting physician. Various techniques have been described to facilitate lead placement, including serial dilation, balloon venoplasty, and percutaneous access medial to the stenosis, each with its own benefits and risks. OBJECTIVE: The purpose of this study was to assess the feasibility, safety, and efficacy of the wire countertraction ("body flossing") technique to facilitate sheath placement through tortuous and stenotic vessels. METHODS: Patients undergoing cardiac implantable electronic device procedures requiring the body flossing technique due to inability to place vascular sheaths over the wire through stenoses or tortuosity were retrospectively analyzed. Clinical characteristics, procedural equipment, and outcomes were analyzed. RESULTS: Simultaneous countertraction was successful in all attempted cases, including 8 patients with stenoses and 2 with tortuosity. In 2 of the stenosis cases, venoplasty had previously failed. No complications occurred. CONCLUSION: Simultaneous countertraction (body flossing) is an effective tool to overcome venous stenosis and tortuosity that are amenable to wire advancement but not to vascular sheaths. It seems to be a safe and effective alternative to other techniques used in these scenarios.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".