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Record W4321453170 · doi:10.1080/00015385.2023.2176043

Leadless Micra pacemaker implantation in patient with previous Senning procedure for dextro-transposition of the great arteries

2023· article· en· W4321453170 on OpenAlexaff
Daniel Lancini, Corey Smith, Osama Elkhateeb, John L. Sapp, Ratika Parkash

Bibliographic record

VenueActa cardiologica. Supplementum · 2023
Typearticle
Languageen
FieldMedicine
TopicCongenital Heart Disease Studies
Canadian institutionsQueen Elizabeth II Health Sciences Centre
Fundersnot available
KeywordsMedicineGreat arteriesVentricleSurgeryCardiologyTricuspid valveInternal medicine

Abstract

fetched live from OpenAlex

Leadless pacemakers have been developed with key advantages over traditional transvenous pacemakers by substantially mitigating the risks of device infection and lead related complications, and providing an alternative pacing strategy in patients with barriers to superior venous access. The Medtronic Micra leadless pacing system is designed for implantation through a femoral venous approach across the tricuspid valve, via Nitinol tine fixation into the trabeculated subpulmonic right ventricle. Patients with surgically corrected dextro-transposition of the great arteries (d-TGA) have an increased risk of pacing requirement. There is limited published experience of implantation of leadless Micra pacemakers in this population, with key challenges relating to trans-baffle access, and deployment of the device into the less trabeculated subpulmonic left ventricle. Here we describe a case report of leadless Micra implantation in a 49 year old male with d-TGA and Senning procedure in childhood, who required pacing for symptomatic sinus node disease, with anatomic barriers to transvenous pacing. Micra implantation was successfully performed following careful consideration of patient anatomy, including the utilisation of 3D modelling to guide the implantation procedure.

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 score0.042
Threshold uncertainty score0.376

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 head0.017
GPT teacher head0.267
Teacher spread0.250 · 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

Citations6
Published2023
Admission routes1
Has abstractyes

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