Treatment and Prevention of Cardiovascular Implantable Electronic Device (CIED) Infections
Bibliographic record
Abstract
An expanded role for cardiac implantable electronic devices (CIEDs) in recent decades reflects an aging population and broader indications for devices, including both primary prevention and management of dysrhythmias. CIED infection is one of the most important device-related complications and has a major impact on mortality, quality of life, healthcare utilization, and cost. Unfortunately, the investigation and management of CIED infection remain complex, often necessitating complete and timely removal of the device and leads in order to eradicate the infection. In addition, the translation of knowledge from an extensive literature to a disparate group of medical practitioners has often been inadequate. This review of CIED infection management highlights the significant advances made during the past decade, including diagnostic criteria, advanced imaging, and next-generation sequencing for culture-negative cases or those in which uncertainty remains. We also outline the role and indication for powered lead extraction, the process of antibiotic choice and treatment duration, considerations related to the timing and location for reimplantation, and preimplantation risk stratification and associated interventions to reduce the risk of CIED infection.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".