Impact of generator replacement on the risk of Fidelis lead fracture
Bibliographic record
Abstract
BACKGROUND: A dilemma arises about the merits of conservative management vs lead replacement and/or extraction when patients with a Medtronic Sprint Fidelis lead undergo generator replacement. Conflicting reports suggest that the fracture rate may increase after generator change. OBJECTIVE: The purpose of this study was to investigate the effect of generator replacement on Fidelis lead performance. METHODS: The Carelink PLUS cohort is composed of 21,500 Fidelis leads (model 6949) implanted in 1,006 centers. The survival rate for leads that remained active after the first generator replacement was compared with that for a control group with matched lead implant duration, patient age, patient sex, and generator type using the Kaplan-Meier method. The control group's starting point was adjusted to match the implant duration of each lead in the replacement group to allow for the comparison of similarly aged leads. RESULTS: Of the 2,988 implanted leads in each group, there was no statistical difference in the number of lead fractures between cases and controls (replacement, n = 227; no replacement, n = 257; Fisher exact, P = .169). Lead survival analysis demonstrated that lead performance since the first replacement procedure did not differ from that of the matched control group. CONCLUSION: The Fidelis lead survival rate after generator replacement does not differ from that of the Fidelis leads that have not had replacement. In the event of generator replacement with no manifestation of lead fracture, the lead model, patient age and life expectancy, ejection fraction, comorbidities, ease of extraction, local extraction expertise, and patient preference should be considered to determine the best course of action.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".