P3872The learning curve associated with the implantation of the nanostim leadless cardiac pacemaker
Bibliographic record
Abstract
Background: The miniaturized Nanostim leadless pacemaker (LP) has become a well-accepted option for selected patients with a single-chamber pacing indication. Use of novel medical technologies, such as LP therapy, may be subjected to a learning curve effect. Purpose: The objective of the current study was to assess the impact of operators' experience on the occurrence of serious adverse device effects (SADE) and procedural efficiency. Methods: Patients implanted with a Nanostim LP (Abbott, USA) within two prospective studies (i.e. LEADLESS ll IDE and Leadless Observational Study) were assessed. Patients were categorized into quartiles based on operator experience. Learning curve analysis included the comparison of SADE rates at 30 days post-implant per quartile and between patients in quartile 4 (operators with most experience; >10 implants) and patients in quartile 1 through 3 (initial operators experience; 1–10 implants). Procedural efficiency was assessed based on procedure duration and repositioning attempts. Results: Nanostim LP implant was performed in 1439 patients by 171 implanters, at 60 centers in 10 countries. A total of 91 (6.4%) patients experienced a SADE in the first 30 days. SADE rates dropped from 7.4% to 4.5% (p=0.038) after more than 10 implants per operator. Total procedure duration, which initially had a mean of 30.9±19.1 minutes in the first quartile, decreased across the procedure quartiles to 21.6±13.2 minutes (p<0.001) in quartile 4 (i.e. most experience). The need for multiple repositionings during the LP procedure reduced in quartile 4 (14.8%), compared to quartile 1 (26.8%; p<0.001), 2 (26.6%; p<0.001) and 3 (20.4%; p=0.03).
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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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".