Laser Lead Extraction: Is There a Learning Curve?
Bibliographic record
Abstract
Laser extraction of device leads offers an attractive alternative to countertraction and electrosurgical dissection sheath, potentially increasing efficacy and reducing complications. Wider adoption of this technology depends on relative ease of use. We report the experience of a new center to define the "learning curve." We performed 76 laser lead extractions in 75 patients (age 63 +/- 17 years, 59 male) from July 2001 to January 2004. Two experienced device implanters who were novice extractors underwent a 2-day site visit to a high volume extraction center for training. Lead extractions were performed in the operating room with immediate surgical backup. The indication for extraction was infection in 39 (systemic in 15), erosion or pain in 11, and lead related or debulking in 25. Complete removal was achieved in 139 of 145 leads (14 ICD, 131 pacemaker). Partial removal (<4 cm retained) was achieved in five leads (4%), and one lead could not be extracted. Complete success was 95% in the first third of patients, 94% in the second third, and 100% in the latter third. Fluoroscopy time fell from 19 +/- 22 minute in the first third of patients to 11 +/- 8 minute in the second third to 8 +/- 4 minute in the latter third (ANOVA P = 0.02). No major complications occurred. Local bleeding required minor left subclavian vein repair in two individuals. Symptomatic venous thrombosis occurred in 3 of the first 11 cases 1-21 days after extraction, but did not occur in the next 64 consecutive patients who received a 1-month anticoagulation regimen (27% vs 0%, P < 0.001). One patient developed venous thrombosis 3 weeks following cessation of warfarin therapy. Practice guidelines reasonably recommend appropriate training prior to independent performance of lead extraction. The current study suggests that experienced device implanters with appropriate operative backup taking a limited, but intensive training program can be safe and effective at lead extraction in a short time, in part a reflection of the improved technology.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".