Cardiovascular implantable electronic device lead extraction
Bibliographic record
Abstract
PURPOSE OF REVIEW: Cardiovascular implantable electronic devices are widely used to treat symptomatic arrhythmias, prevent sudden cardiac death, and improve symptoms and cardiac function. Continued population growth and expanding indications have resulted in a progressive increase in the number of cardiovascular implantable electronic device implantations. Mirroring this growth, an increasing number of leads require removal because of a variety of indications. Transvenous lead extraction continues to evolve with better techniques and risk-management strategies. This review highlights the indications, techniques, procedural outcomes, and future directions of arrhythmia device management and extraction. RECENT FINDINGS: Indications for extractions are reviewed in light of newly published data. Same day contralateral reimplantation has been shown to be safe in patients with localized pocket infection. Alternative extraction techniques, utilizing the femoral and internal jugular veins, provide additional routes for device removal as stand-alone procedures or in cases of difficult extraction via the subclavian vein. Preprocedural imaging to identify adherence sites and cardiac perforation can help to reduce complications. Routine capsulectomy at generator change does not seem to reduce the risk of device infection, and multiple trials are underway to assess other methods of reducing infections as part of a lead management strategy. SUMMARY: Improvement in technology, alternative routes of extraction and preprocedural imaging continue to add to procedural efficacy and reduce complication rates of lead extraction.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| 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 | 0.001 |
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".