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Record W4224283073 · doi:10.1016/j.hrcr.2022.04.012

Late ventricular pacemaker lead perforation after electrical cardioversion—A case report

2022· article· en· W4224283073 on OpenAlexaff
Bert Vandenberk, Sevan Letourneau-Shesaf, Jillian Colbert, Glen Sumner, Vikas Kuriachan

Bibliographic record

VenueHeartRhythm Case Reports · 2022
Typearticle
Languageen
FieldMedicine
TopicCardiac pacing and defibrillation studies
Canadian institutionsLibin Cardiovascular Institute of AlbertaUniversity of Calgary
FundersServierMedtronicNovartis
KeywordsMedicinePericardial effusionPerforationLead (geology)ImplantCardiologyCardioversionEffusionImplantable cardioverter-defibrillatorInternal medicineRadiologySurgeryAtrial fibrillation

Abstract

fetched live from OpenAlex

Key Teaching Points•Late lead perforation, >1 month after implant or upgrade, is rare but can present at all times.•Lead perforation should be suspected in case of pleuritic symptoms, a new pericardial effusion, or acute changes in lead measurements upon device interrogation, including unipolar lead measurements.•Lead perforation diagnosis can be confirmed on echocardiogram or computed tomography scan.•Increased awareness for complications shortly after electrical cardioversion is recommended.•The right ventricular lead should be implanted in a septal position, which can be verified in the left anterior oblique view during implant. •Late lead perforation, >1 month after implant or upgrade, is rare but can present at all times.•Lead perforation should be suspected in case of pleuritic symptoms, a new pericardial effusion, or acute changes in lead measurements upon device interrogation, including unipolar lead measurements.•Lead perforation diagnosis can be confirmed on echocardiogram or computed tomography scan.•Increased awareness for complications shortly after electrical cardioversion is recommended.•The right ventricular lead should be implanted in a septal position, which can be verified in the left anterior oblique view during implant.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.138
Threshold uncertainty score0.774

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.014
GPT teacher head0.271
Teacher spread0.257 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designCase report
Domainnot available
GenreEmpirical

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

Citations4
Published2022
Admission routes1
Has abstractyes

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