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Record W4410122364 · doi:10.24908/pocusj.v10i01.18073

Troubleshooting Transvenous Pacemakers with Point of Care Ultrasound (POCUS)

2025· article· en· W4410122364 on OpenAlexvenueno aff
Emily Gohde, Seth Lotterman, Ikram Irfanullah, David T. Hansen, Felix Pacheco, Adam Wise, Matthew Tichauer, Trent She

Bibliographic record

VenuePOCUS Journal · 2025
Typearticle
Languageen
FieldMedicine
TopicCardiac Arrhythmias and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsTroubleshootingPoint of care ultrasoundProtocol (science)MedicineEmergency departmentEmergency physicianMedical emergencyPoint of careComputer sciencePathologyNursingAlternative medicine

Abstract

fetched live from OpenAlex

Emergent transvenous pacing can be performed for patients with an unstable bradyarrhythmia in the emergency department. While emergent transvenous pacemaker (TVP) placement is performed relatively rarely, its importance in the management of these patients requires emergency physicians to be familiar with the procedure. Point of care ultrasound (POCUS) is traditionally used for the initial vascular access associated with TVP placement but can also be indispensable in the subsequent placement and advancement of the TVP wire itself. This article discusses two cases and then outlines a general protocol that incorporates POCUS into a standard emergent TVP procedure. Further, we outline some common POCUS troubleshooting tactics to improve success and ways in which POCUS can rapidly identify complications.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.197
Threshold uncertainty score0.427

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.004
GPT teacher head0.251
Teacher spread0.248 · 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 designObservational
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

Citations0
Published2025
Admission routes1
Has abstractyes

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