Advanced left-ventricular lead placement techniques for cardiac resynchronization therapy
Bibliographic record
Abstract
PURPOSE OF REVIEW: Due to complex venous anatomy and limitations in lead delivery tools and technology, the incidence of failed left-ventricular lead implants continues to be as high as 10%. RECENT FINDINGS: A move towards an interventional approach to left-ventricular lead implantation has provided viable alternatives to surgical lead implantation. The use of telescoping sheaths, gooseneck snares and percutaneous balloon venoplasty may reduce procedural times by facilitating lead delivery despite challenging venous anatomy. In addition, recent advancements in left-ventricular lead technology now allow implanting physicians to overcome commonly encountered obstacles such as high thresholds and phrenic nerve stimulation, without having to move the lead from a stable position. For those with suboptimal or inaccessible coronary vein targets, a simplified transseptal endocardial implant approach has also been described. SUMMARY: These recent advances in implant techniques and left-ventricular lead technology provide promising solutions to commonly encountered procedural obstacles in the implementation of resynchronization therapy. These alternative strategies will hopefully reduce the rate of failed implants and referrals for surgical epicardial leads.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".