Bibliographic record
Abstract
Émile Saliba,1 Emmanuelle Massie,2 Ying Tung Sia3 1Cardiology Department, Hôtel-Dieu de Montréal, Montréal, QC, Canada; 2Internal Medicine Department, Hôpital Saint Luc, Montréal, QC, Canada; 3Cardiology Department, Hôpital Pierre Boucher, Longueuil, QC, Canada Abstract: Cardiac implantable electronic devices (CIEDs) are being used more and more often nowadays. Indications have grown, and access to implantation facilities has increased as well. These devices are often lifesaving, and they can be associated with many other benefits. However, as with any medical procedure, complications can occur. In fact, CIED infection is a prevalent complication that can cause high morbidity and can even lead to death. It is important that most clinicians be familiar with signs and symptoms associated with CIED infection as early diagnosis and treatment lead to better outcomes. Nonetheless, the prevention of such infections remains the cornerstone in the management of CIED-related infections. In this paper, we will review in detail the most significant risk factors that can lead to CIED infection. We will also explore the different available tools that can help decrease the incidence of this complication. In addition, we will summarize the different treatment modalities and the major prevention methods. Keywords: cardiac implantable electronic device, infection, pacemaker, endocarditis
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 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".