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Record W2041321363 · doi:10.1186/s13104-015-1034-y

Troubleshooting during a challenging high-risk pacemaker lead extraction: a case report and review of the literature

2015· review· en· W2041321363 on OpenAlexaff
Jacques Rizkallah, William Kent, Vikas Kuriachan, John H. Burgess, Derek V. Exner

Bibliographic record

VenueBMC Research Notes · 2015
Typereview
Languageen
FieldMedicine
TopicCardiac pacing and defibrillation studies
Canadian institutionsLibin Cardiovascular Institute of AlbertaUniversity of Calgary
Fundersnot available
KeywordsTroubleshootingLead (geology)MedicineComputer scienceRisk analysis (engineering)Data scienceBiology

Abstract

fetched live from OpenAlex

BACKGROUND: The use of cardiac implantable electrical devices continues to increase with the validation of new beneficial indications. While the risks of device implantation decreased significantly over time, significant risk remains associated with their extraction when indicated. A high-risk pacemaker lead extraction case is described, wherein a chronically implanted lead that had perforated the right atrium was successfully removed without the need for cardiopulmonary bypass. In this report we share our approach to this challenging extraction case and describe an infrequently utilized off-pump hybrid technique that we term the "lead-inverting stitch". CASE PRESENTATION: A 74 year-old Caucasian woman with complete heart block and remote pacemaker implantation presents with a swollen and erythematous infected pacemaker pocket necessitating device extraction. Chest computerized tomographic imaging revealed a chronically perforating right atrial lead tip approximately 2 cm within the pericardial space. A successful hybrid transvenous and open surgical extraction approach was undertaken without the need for cardiopulmonary bypass; this was made possible due to a successfully positioned "lead-inverting stitch". CONCLUSION: Implantable cardiac electrical device infections are amongst the most dreaded post implant complications. Risks of device extraction are further complicated in cases of chronic lead perforations. Extraction strategies that avoid cardiopulmonary bypass initiation are preferred.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.004
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.269
GPT teacher head0.494
Teacher spread0.224 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations2
Published2015
Admission routes1
Has abstractyes

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