Incidence of premature battery depletion in subcutaneous cardioverter-defibrillator patients: insights from a multicenter registry
Bibliographic record
Abstract
BACKGROUND: The subcutaneous ICD established its role in the prevention of sudden cardiac death in recent years. The occurrence of premature battery depletion in a large subset of potentially affected devices has been a cause of concern. The incidence of premature battery depletion has not been studied systematically beyond manufacturer-reported data. METHODS: Retrospective data and the most recent follow-up data on S-ICD devices from fourteen centers in Europe, the US, and Canada was studied. The incidence of generator removal or failure was reported to investigate the incidence of premature S-ICD battery depletion, defined as battery failure within 60 months or less. RESULTS: Data from 1054 devices was analyzed. Premature battery depletion occurred in 3.5% of potentially affected devices over an observation period of 49 months. CONCLUSIONS: The incidence of premature battery depletion of S-ICD potentially affected by a battery advisory was around 3.5% after 4 years in this study. Premature depletion occurred exclusively in devices under advisory. This is in line with the most recently published reports from the manufacturer. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT04767516 .
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".