P708Combined transvenous lead extraction and baffle stenting in adults with transposition of the great arteries and systemic baffle leak or obstruction following atrial switch
Bibliographic record
Abstract
Background: Management of baffle leak or stenosis in patients status post atrial switch for transposition of the great arteries (TGA) is challenging in presence of a transvenous pacing or defibrillator system and often requires transvenous lead extraction (TLE). Also, an evaluation of baffle integrity is required every time an upgrade or a revision of the pacing/defibrillator system is planned. Purpose: Report technical challenges, safety and outcomes of a stepwise combined percutaneous approach including TLE followed by baffle stenting and device re-implantation in TGA with atrial switch. Methods: All consecutive procedures of TLE followed by a baffle percutaneous intervention in patients with TGA and atrial switch performed in our institution between August 2002 and February 2018 were included. Results: A total of 19 (17 pacing leads, 2 defibrillation leads) leads were extracted during 10 procedures (10 patients, median age 27 years [15–48], 50% male, median of 2 leads per patient [1–3]). Main indications for TLE were lead dysfunction (50%) and upgrade (20%). Median time from implantation to extraction was 7 years [4.6–13.3]. A laser sheath was used in 5 (50%) of the procedures and a mechanical method in 2 (20%). Complete TLE was achieved in every patient. Post-extraction angiography confirmed the presence of baffle stenosis in 66% and baffle leak in 34%, which were addressed by baffle stenting with an acute success in every case. Immediate re-implantation of a transvenous lead through the stent was performed in 66% of the procedures. Adverse outcomes included 1 tricuspid valve damage requiring surgery 3 months after the procedure. During a median follow-up time of 6.8 years (1 month – 17 years), there was no recurrence of baffle stenosis or leak neither device infection.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".