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Record W4407358116 · doi:10.1016/j.tcm.2025.02.001

Cardiac implantable electronic device infection

2025· review· en· W4407358116 on OpenAlexaff
Simon Christie, Michael McGregor, Andrew D. Krahn

Bibliographic record

VenueTrends in Cardiovascular Medicine · 2025
Typereview
Languageen
FieldMedicine
TopicCardiac pacing and defibrillation studies
Canadian institutionsUniversity of British Columbia HospitalUniversity of ManitobaUniversity of British Columbia
Fundersnot available
KeywordsMedicineCardiologyInternal medicine

Abstract

fetched live from OpenAlex

Cardiac implantable electronic device infections (CIEDI) are an important complication of device implantation associated with significant morbidity, mortality, and cost to the healthcare system. Identifying patients at high risk of device infection is paramount to improving decision making. This includes selecting appropriate devices and implementing adjunctive infection prevention measures, such as antimicrobial envelopes. In addition to meticulous antiseptic surgical technique, several other procedure-related practices can help reduce the risk of device infection. Developing expert centers with multidisciplinary teams capable of device extraction is important to manage patients with CIEDI. In this review, we aim to provide the reader with a succinct overview of CIEDI and summarize new evidence for risk assessment, prevention, diagnosis, and infection management.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.961
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0090.004
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.049
GPT teacher head0.381
Teacher spread0.332 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations5
Published2025
Admission routes1
Has abstractyes

Explore more

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